Data Engineer Azure

Pranathi Chennoju_Resume

Senior Azure Data Engineer

Phone: +1-313-***-**** Email:*****************@*****.***

PROFESSIONAL SUMMARY:

Sr. Azure Data Engineer with 10 years of experience in designing and implementing scalable data ingestion pipelines using Microsoft Azure Cloud, Python, PySpark, Big Data.

Experienced data professional with a strong background in end-to-end management of ETL data pipelines, ensuring scalability and smooth operations.

Proficient in optimizing query techniques and indexing strategies to enhance data fetching efficiency.

Skilled in utilizing SQL queries, including DDL, DML, and various database objects, for data manipulation and retrieval.

Expertise in integrating on-premises and cloud-based data sources using Azure Data Factory, applying transformations, and loading data into Snowflake.

Strong knowledge of data warehousing techniques, including data cleansing, Slowly Changing Dimension handling, surrogate key assignment, and change data capture for Snowflake modeling.

Experienced in designing and implementing scalable data ingestion pipelines using tools such as Apache Kafka, Apache Flume, and Apache Nifi.

Proficient in developing and maintaining ETL/ELT workflows using technologies like Apache Spark, Apache Beam, or Apache Airflow for efficient data extraction, transformation, and loading processes.

Skilled in implementing data quality checks and cleansing techniques to ensure data accuracy and integrity throughout the pipeline.

Created custom Kibana dashboards for visualizing and monitoring operational and business-critical metrics.

Implemented log aggregation solutions using Elasticsearch, Logstash, and Kibana (ELK Stack) for centralized observability.

Tuned sharding, replication, and indexing strategies to optimize Elasticsearch performance and cost efficiency

Experienced in building and optimizing data models and schemas using technologies like Apache Hive, Apache HBase, or Snowflake for efficient data storage and retrieval for analytics and reporting.

Strong proficiency in developing ELT/ETL pipelines using Python and Snowflake Snow SQL.

Skilled in creating ETL transformations and validations using Spark-SQL/Spark Data Frames with Azure Databricks and Azure Data Factory.

Designed and implemented Azure Semantic Models to enhance data comprehension and optimize analytical queries in Power BI.

Developed and managed tabular models in Azure Analysis Services (AAS), ensuring efficient data modeling and performance tuning.

Integrated Azure AI services with semantic models to enable intelligent data retrieval and insights.

Created and optimized data relationships, hierarchies, and measures using DAX (Data Analysis Expressions) for advanced analytics.

Detail-oriented Data Steward with expertise in data governance, quality management, and metadata management, ensuring accuracy, consistency, and compliance across enterprise data assets.

Extensive experience in data lineage, data cataloging, and master data management (MDM) to support business intelligence and analytics initiatives.

Strong background in Electronic Health Records (EHR HL7) solutions, ensuring seamless healthcare data interoperability and compliance.

Experience working with Oracle, Snowflake, SQL Server, and other RDBMS, including schema design, indexing, query optimization, and performance tuning.

Experience writing and optimizing stored procedures using Oracle PL/SQL, SQL Server T-SQL, and Snowflake SQL for complex data processing and transformations.

Experience with GitHub, SVN, and similar source control systems, ensuring code versioning, collaboration, and CI/CD integration.

Experience handling structured and unstructured data, including ETL pipelines, data ingestion, and transformation workflows using modern data engineering frameworks.

Expertise in HL7 and FHIR standards, processing and integrating EHR data from various healthcare systems while ensuring compliance with interoperability standards.

Experience working with AWS Glue, PySpark, and Step Functions for building scalable and efficient cloud-based data pipelines.

Designed and implemented data pipelines for EHR systems, ensuring seamless data exchange across healthcare platforms.

Developed and optimized HL7 message processing workflows (ADT, ORU, MDM, etc.) for interoperability between healthcare providers.

Designed, built, and optimized Anaplan models to support business planning and forecasting processes.

Developed Anaplan modules, lists, dashboards, and actions to enhance financial and operational planning.

Integrated Anaplan with SAP, Salesforce, Workday, Snowflake, and other enterprise systems using APIs and Anaplan Connect.

Created Anaplan UX dashboards for improved user experience and data visualization.

Expertise in Java for developing high-performance ETL pipelines, data processing frameworks, and backend systems.

Strong experience with AWS Glue (Java-based ETL development) for large-scale data transformation.

Collaborative team member, working closely with Azure Logic Apps administrators and DevOps engineers to monitor and resolve issues related to process automation and data processing pipelines.

Experienced in optimizing code for Azure Functions to extract, transform, and load data from diverse sources.

Strong experience in designing, building, and maintaining data integration programs within Hadoop and RDBMS environments.

Developed data wrangling and transformation pipelines using Pandas for large-scale datasets.

Optimized data processing workflows by leveraging vectorized operations and multi-threading.

Performed data cleaning, merging, aggregation, and feature engineering for analytical models.

Utilized Pandas DataFrames and Series to handle structured data efficiently.

Designed ETL (Extract, Transform, Load) pipelines with Pandas for data ingestion and preprocessing.

Configured Azure Device Provisioning Service (DPS) for automated and secure onboarding of IoT devices at scale.

Proficient in implementing CI/CD frameworks for data pipelines using tools like Jenkins, ensuring efficient automation and deployment.

Experience with Apache Spark (via PySpark in AWS Glue) for distributed data transformation and analytics.

Skilled in executing Hive scripts through Hive on Spark and SparkSQL to address various data processing needs.

Collaborative team member, ensuring data integrity and stable data pipelines while collaborating on ETL tasks.

Strong experience in utilizing Kafka, Spark Streaming, and Hive to process streaming data, developing robust data pipelines for ingestion, transformation, and analysis.

Proficient in utilizing Spark Core and Spark SQL scripts using Scala to accelerate data processing capabilities.

Experienced in utilizing JIRA for project reporting, task management, and ensuring efficient project execution within Agile methodologies.

Experienced in loading data to Hive partitions and created buckets in Hive and developed Map Reduce jobs to automate transfer the data from H-Base.

Extensive experience in processing and transforming diverse file formats, including CSV, JSON, XML, ORC, Avro, and Parquet, utilizing Spark with Python, PySpark, and Scala to enhance data analysis and development workflows.

Strong expertise across the technology stack, encompassing ETL, data analysis, cleansing, matching, quality, audit, and design, while also possessing extensive experience in designing and optimizing OLAP and OLTP systems for high-performance database environments

Collaborated with DevOps engineers to developed automated CI/CD and test-driven development pipeline using azure as per the client requirement.

Orchestrated complex data workflows using AWS Step Functions, integrating AWS Glue, Lambda, and S3.

Optimized AWS Glue jobs by leveraging partitioning, dynamic frames, and push-down predicates to enhance performance.

Built event-driven data processing pipelines using AWS services, ensuring real-time or batch data processing capabilities

Created and maintained HiveQL scripts and jobs using tools such as Apache Oozie and Apache Airflow.

Working with JIRA to report on Projects, and creating sub tasks for Development, QA, and Partner validation.

Experience in full breadth of Agile ceremonies, from daily stand-ups to internationally coordinated PI Planning

TECHNICAL SKILLS:

Azure Cloud Services

Azure Data Lake, Delta lake, Azure Data Factory, Azure Databricks, Application Insights, Key Vault, AzureBlobStorage, EventHub, Logic Apps, Functional Apps, Snowflake.

Big Data Technologies

HDFS, Yarn, Map Reduce, Hive, SQOOP, Flume, HBase, PySpark, Kafka

Web Technologies

HTML5, CSS3, XML, JDBC, JSP, RestAPI.

Databases

My SQL Server, Teradata, Oracle 11g/12c, MySQL, NoSQL, Cassandra, Cosmos DB, DB2.

Languages

Python, Scala, SQL.

Version Control Tools

SVM, GitHub, Bitbucket, GitLab

Hadoop Distribution

Cloudera, Horton Works

Visualization Tools

Power BI, Tableau.

ETL Tools

Informatica, SSIS, SSRS.

IDE & Build tools

PyCharm, Visual Studio.

EDUCATION:

Master’s in computer science.

Bachelors of computer science.

WORK EXPERIENCE:

Verizon Texas Irving, Jan 2024 to Present

Senior Azure Data Engineer

Responsibilities:

Worked on creating tabular models on Azure analytic services for meeting business reporting requirements.

Data Ingestion to one or more cloud Azure Services - (Azure Data Lake, Azure Storage, Azure SQL,

Azure DW) and cloud migration processing the data in Azure Databricks.

Creating pipelines, data flows and complex data transformations and manipulations using ADF and PySpark with Databricks.

Used Data Flow debug for effectively building ADF data flow pipelines. Improved performance by using optimization options by effectively using partitions during various transformations.

Created complex ETL Azure Data Factory pipelines using mapping data flows with multiple Input/output transformations.

Worked on Azure BLOB and Data Lake storage and loading data into Azure SQL Synapse analytics (DW).

Worked with Azure SQL Database Import and Export Service.

Used Azure Key vault as central repository for maintaining secrets and referenced the secrets in Azure Data Factory and in Databricks notebooks.

Developed KPI dashboards and reports using Power BI, powered by Azure Semantic Models for enterprise-wide analytics.

Optimized query performance by implementing aggregation strategies, partitions, and indexing in semantic models.

Automated model deployment and versioning using Azure DevOps CI/CD pipelines for streamlined data model updates.

Integrated Azure Data Lake, Synapse Analytics, and Databricks with Azure Semantic Models to enable large-scale data analytics.

Implemented high-performance numerical computing using NumPy arrays and broadcasting techniques.

Optimized computational efficiency with NumPy’s vectorized operations, avoiding explicit loops.

Performed linear algebra operations, statistical analysis, and numerical simulations using NumPy.

Utilized multi-dimensional arrays (ndarrays) for scientific computing and machine learning applications.

Integrated NumPy with Pandas, Matplotlib, and SciPy for data analysis and visualization

Built a common SFTP download or upload framework using Azure Data Factory and Databricks.

Developed Databricks ETL pipelines using notebooks, Spark Data frames, SPARK SQL, and python scripting.

Created Databricks Job workflows which extracts data from SQL server and upload the files to SFTP using PySpark and python.

Utilized Azure Device Twins for maintaining device metadata, properties, and state synchronization.

Designing AI-driven ETL pipelines, automating data enrichment, and leveraging LLMs for intelligent data processing

Implemented real-time device state monitoring and updates through Azure IoT services.

Developed serverless Azure Functions to process IoT data, trigger workflows, and integrate with cloud storage.

Designed event-driven functions using Event Grid, Service Bus, and HTTP triggers for real-time data processing

Build the Logical and Physical data model for snowflake as per the changes required.

Define virtual warehouse sizing for Snowflake for different type of workloads.

Worked on Oracle Databases, RedShift, and Snowflakes

Developed Spark applications using PySpark and Spark-SQL for data extraction, transformation, and aggregation from multiple file formats.

Hands-on with Azure OpenAI, Azure Machine Learning, and Cognitive Search for deploying and managing AI models at scale.

Responsible for estimating the cluster size, monitoring, and troubleshooting of the Spark Databricks cluster.

Developed Spark code using Scala and Spark-SQL/Streaming for faster processing of data.

Used Spark Streaming to divide streaming data into batches as an input to spark engine for batch processing.

Develop transformation logic using snow pipeline. Hands-on experience with Snowflake utilities, Snow SQL, Snow Pipe, Big Data model techniques using Python/ Scala.

Responsible for Building and scaling ETL / Event processing systems that organize data and manage complex rule sets in batch and real-time.

Experience in automating ETL workflows and data transformations using Python, SQL, and cloud-based orchestration tools.

Experience in processing and integrating healthcare data using FHIR (Fast Healthcare Interoperability Resources) for seamless interoperability between systems.

Knowledge of FHIR APIs, resources, and data models, enabling efficient exchange and retrieval of clinical data.

Experience in transforming and mapping HL7 v2 messages to FHIR for modern healthcare applications.

Skilled in developing and optimizing data models for analytical and reporting use cases, enabling business intelligence and decision-making.

Experience with Elasticsearch and Kibana, designing search and visualization solutions for large-scale datasets.

Hands-on experience integrating vector databases, embedding models, and AI agent frameworks into data pipelines.

Experience leveraging Azure AI services for AI-driven analytics and automation

ETL pipelines in and out of data warehouse using combination of Python and Snowflakes Snow SQL Writing SQL queries against Snowflake.

Involved in Branching, Tagging, Release Activities on GitHub Version Control.

Environment: MS SQL,Tableau, Oracle, Spark, SQL, DBT, Python, Scala, Spark, shall scripting, RestAPI’s, GIT, JIRA, Jenkins, Kafka, ADF Pipeline, Power Bi.

State of Wisconsin (Work Force Development ;

Responsibilities:

Analysed data from Azure data storages using Databricks and Spark cluster capabilities, extracting valuable insights from large datasets.

Perform all phases of software engineering including requirements analysis, application

Developed and maintained end-to-end operations of ETL data pipeline and worked with large data sets in azure data factory.

Increased the efficiency of data fetching by using queries for optimizing and indexing.

Developed custom activities using Azure Functions, Azure Databricks, and PowerShell scripts to perform data transformations, data cleaning, and data validation.

Hands on experience in using Kafka, Spark streaming, to process the streaming data in specific use cases.

Developed and deployed SSIS/SSRS packages for data extraction, transformation, and reporting, resulting in improved data accuracy and timely delivery of business insights.

Implemented Azure EventHub for real-time data ingestion, enabling efficient streaming and processing of high-volume data.

Containerized data processing applications using Docker, ensuring portability, scalability, and easier deployment across different environments.

Managed end-to-end Big Data flow within the application, including data ingestion from upstream sources to HDFS, as well as processing and analysis of data in HDFS.

Designed and implemented Spark Streaming solutions for real-time data processing and analysis, enabling immediate insights and actionable intelligence.

Developed end-to-end data pipelines, integrating multiple data sources such as DB2, SQL, Oracle, flat files (csv, delimited), APIs, XML, and JSON, for comprehensive data processing and analysis.

Utilized Terraform to automate infrastructure provisioning and management, ensuring consistent and reproducible deployments in an Azure environment.

Leveraged Azure DevOps for continuous integration and deployment (CI/CD) of data pipelines and applications, streamlining the development and deployment processes.

Configured data pipeline orchestration using YAML pipelines in Azure DevOps, ensuring efficient and reliable execution of data workflows.

Conducted data validation, cleansing, and transformation processes, leveraging the power of SSIS/SSRS packages and SQL scripts to ensure data accuracy and integrity.

Wrote SQL queries using programs such as DDL, DML and indexes, triggers, views, stored procedures, functions and packages.

Worked closely with the data engineering team to enhance and optimize data pipelines, improving data processing speed and efficiency.

Documented technical specifications, data flow diagrams, and process documentation to ensure clear communication and knowledge transfer within the team.

Actively participated in code reviews, troubleshooting, and performance tuning sessions to improve overall system performance and reliability.

Worked with Azure Logic Apps administrators to monitor and troubleshoot issues related to process automation and data processing pipelines.

Developed and maintained data lineage tracking in Purview to ensure end-to-end visibility of data flow across Azure services.

Deployed ADF pipelines in the production environment by monitoring, managing and optimizing data solutions.

Worked on Azure synapse to architect and execute advanced analytics, enabling predictive analytics and data-driven insights.

Environment: Azure Databricks, Azure Data Factory, Snowflake, Logic Apps, Functional App, GCP, Big Query, Cloud SQL, Snowflake,, SQL, DBT, Python, Scala, Spark, GIT, JIRA, Jenkins, Kafka, ADF Pipeline, Power Bi.

Fifth Third Bank Cincinnati Ohio: June 2020 to August 2022

Senior Big Data Engineer :

Responsibilities:

Designed and implemented scalable data ingestion pipelines using Azure Data Factory, efficiently ingesting data from diverse sources such as SQL databases, CSV files, and REST APIs.

Developed robust data processing workflows leveraging Azure Databricks and Spark for distributed data processing and transformation tasks.

Ensured data quality and integrity through comprehensive data validation, cleansing, and transformation operations performed using Azure Data Factory and Databricks.

Ingested data into Databricks Delta tables and implemented efficient data loading strategies, considering factors like partitioning and clustering.

Developed real-time data streaming capabilities into Snowflake by seamlessly integrating Azure Event Hubs and Azure Functions, enabling prompt and reliable data ingestion.

Leveraged Azure Synapse Analytics to seamlessly integrate big data processing and analytics capabilities, empowering data exploration and insights generation.

Orchestrated Delta Lake pipelines using Azure Data Factory (ADF) and Azure Synapse Analytics.

Implemented Azure Virtual Machines (VMs), Azure Kubernetes Service (AKS), and Azure Container Instances (ACI) for containerized data workloads.

Conducted data governance assessments using Microsoft Purview to identify and mitigate potential data risks.

Orchestrated end-to-end data pipelines using Azure Data Factory (ADF) with Databricks as a processing engine.

Established monitoring and alerting for Fabric workloads using Azure Monitor and Log Analytics.

Ensured data security and access control using OneLake’s role-based permissions and Microsoft Purview integration.

Used cloud shell SDK in GCP to configure the services Data Proc, Storage, BigQuery and coordinated with team and Developed framework to generate Daily adhoc reports and Extracts from enterprise data from BigQuery.

Work on entire life cycle of the Salesforce migration project, gathering the requirements and planning the design, through the implementation phase. Code the transformation and follow good attributes of data migration.

Automated data pipelines and workflows by configuring event-based triggers and scheduling mechanisms, streamlining data processing and delivery which resulted in 48% reduction in manual intervention.

Developed and deployed Azure Functions to handle critical data preprocessing, enrichment, and validation tasks within the data pipelines, elevating the overall data quality and reliability.

Built scalable data warehouse solutions using Fabric Warehouse and OneLake.

Utilized Azure DevOps Git Repositories to store and manage code for data pipelines and other scripts.

Implemented partitioning strategies in Azure to enhance query performance and reduce processing time.

Implemented comprehensive data lineage and metadata management solutions, ensuring end-to-end visibility and governance over data flow and transformations.

Designed ETL/ELT workflows for ingesting data from diverse sources into OneLake.

Conducted meticulous performance tuning and capacity planning exercises, ensuring scalability and maximizing efficiency within the data infrastructure.

Developed and fine-tuned high-performance PySpark jobs to handle complex data transformations, aggregations, and machine learning tasks on large-scale datasets.

Utilized Spark core and Spark SQL scripts using Scala to expedite data processing and enhance performance.

Proficiently worked within Agile methodologies, actively participating in daily stand-ups and coordinated planning sessions.

Environment: Azure Databricks, Data Factory, Azure Storage, Key vault, Logic Apps, Functional App, Snowflake, MS SQL, Oracle, HDFS, MapReduce, YARN, Spark, Hive, SQL, Python, Scala, PySpark, GIT, JIRA, Jenkins, Kafka, ADF Pipeline, Rest APIs.

LTI Mindtree(Seattle Fire Department) Seattle, Washington. April 2018 to May 2020

Big Data Developer

Responsibilities:

Demonstrated hands-on experience in Azure Cloud Services, including Azure Synapse Analytics, SQL Azure, Data Factory, Azure Analysis Services, Application Insights, Azure Monitoring, Key Vault, and Azure Data Lake.

Created batch and streaming pipelines in Azure Data Factory (ADF) using Linked Services, Datasets, and Pipelines to efficiently extract, transform, and load data.

Developed Azure Data Factory (ADF) batch pipelines to ingest data from relational sources into Azure Data Lake Storage (ADLS Gen2) in an incremental fashion, applying necessary data cleansing and subsequently loading it into Delta tables.

Implemented Azure Logic Apps to trigger automated processes upon receiving new emails with attachments, efficiently loading the files into Blob storage.

Implemented CI/CD pipelines using Azure DevOps in the cloud, utilizing GIT, Maven, and Jenkins plugins for seamless code integration and deployment.

Built a Spark Streaming application for real-time analytics on streaming data, leveraging Spark SQL to query and aggregate data in real-time and visualize the results in Power BI or Azure Data Studio.

Developed Spark Streaming applications that integrate with event-driven architectures such as Azure Functions or Azure Logic Apps, processing events in real-time and triggering downstream workflows based on the results.

Designed and implemented data pipelines using Lambda functions to ingest streaming data from various sources.

Designed Azure SQL Database, Azure Synapse Analytics, and Cosmos DB infrastructure for optimized data storage.

Utilized AWS S3 for temporary storage of raw data and checkpointing and AWS Redshift for complex transformations and aggregations.

Assisted in auditing and compliance reporting by leveraging Purview insights on data classification and access patterns.

Configured and managed Databricks clusters, including auto-scaling, cluster policies, and cost optimization.

Integrated Power BI Direct Lake mode with OneLake for real-time data analytics.

Utilized AWS SQS for decouple data ingestion from processing for scalability and reliability.

Defined and enforced data access controls based on user roles and permissions RBAC.

migrating legacy application's datastore to Elasticsearch.

Migrated ETL processes from Oracle to Hive, testing and validating the ease of data manipulation and processing in Hive.

Managed data lifecycle and retention policies in OneLake to control storage costs.

Debugging data pipeline issues, optimizing performance within PostgreSQL and across the data ecosystem, and scaling infrastructure as needed.

Migrated legacy SQL-based ETL pipelines to Azure Databricks for enhanced performance and scalability.

Developed a PySpark job in Scala to index data into Azure Functions from external Hive tables stored in HDFS.

Utilized HiveQL to analyze partitioned and bucketed data, optimizing query performance and efficiency.

Developed and executed Hive queries on analyzed data for aggregation and reporting purposes.

Developed Sqoop jobs to efficiently load data from RDBMS into external systems like HDFS and Hive.

Developed Spark applications using PySpark and Spark SQL for data extraction, transformation, and aggregation from multiple file formats, ensuring efficient data processing.

Developed Fabric Dataflows for self-service data preparation and OneLake integration.

Implemented Spark scripts using Scala and Spark SQL to access Hive tables for faster data processing.

Loaded data from UNIX file systems to HDFS, ensuring data availability for further processing and analysis.

Configured Spark Streaming to receive real-time data from Apache Flume and stored the stream data in Azure Tables using Scala.

Utilized Spark RDD transformations to filter data and enhance SparkSQL processing capabilities.

Utilized Hive Context and SQL Context to integrate Hive metastore and SparkSQL for optimized performance and data processing.

Utilized GIT as the version control system to access repositories and coordinate with CI tools for effective code management and collaboration.

Environment: Azure Data Factory, Azure Synapse Analytics, Azure DevOps, AWS S3, AWS Redshift, AWS Glue, AWS, Sqoop, HDFS, Power BI, Git, Zookeeper, Flume, Kafka, Apache PySpark, SparkSQL, Scala, Hive, Hadoop, Cloudera, HBase, HiveQL, MySQL

IBM Birmingham, AL May 2016 to Mar 2018

Data Warehouse Developer

Responsibilities:

Actively participated in Agile Scrum Methodology, engaging in daily stand-up meetings. Proficiently utilized Visual SourceSafe for Visual Studio 2010 for version control and effectively managed project progress using Trello.

Implemented advanced reporting functionalities in Power BI, including Drill-through and Drill-down reports with interactive Drop-down menus, data sorting capabilities, and subtotals for enhanced data analysis.

Employed Data warehousing techniques to develop a comprehensive Data Mart, serving as a reliable data source for downstream reporting. Developed a User Access Tool empowering users to create ad-hoc reports and execute queries for in-depth analysis within the proposed Cube.

Managed and updated Erwin models for logical/physical data modeling of Consolidated Data Store (CDS), Actuarial Data Mart (ADM), and Reference DB to meet user requirements.

Utilized TFS for source controlling and tracking environment-specific script deployments.

Exported current data models from Erwin to PDF format and publishing them on SharePoint for user access.

Wrote triggers, stored procedures, and functions using Transact-SQL (T-SQL) and maintained physical database structures.

Deployed scripts in different environments based on Configuration Management and Playbook requirements.

Created and managed files and file groups, establishing table/index associations, and optimizing query and performance tuning.

Maintained users, roles, and permissions within the SQL Server environment.

Automated report generation and Cube refresh processes by creating SSIS jobs, ensuring the timely and accurate delivery of critical information.

Excelled in deploying SSIS Packages to production, leveraging various configuration options to export package properties and achieve environment independence.

Utilized SQL Server Reporting Services (SSRS) to author, manage, and deliver comprehensive reports, both in print and interactive web-based formats.

Developed robust stored procedures and triggers to enforce data consistency and integrity during data entry operations.

Environment: MS SQL Server 2008/2012, Visual Studio 2010, SSIS, SSRS, SSAS, Share point, Profiler, MS Office, MS Access, Git.

Client: Aetna Inc., Hartford, CT Mar 2014 – Apr 2016

Role: Data Warehouse Developer

Developed complex stored procedures, efficient triggers, and necessary functions, along with creating indexes and indexed views to optimize performance in SQL Server.

Extensive experience in monitoring and tuning SQL Server performance, employing best practices to ensure optimal database performance.

Expertise in designing ETL data flows using SSIS, creating mappings and workflows for extracting data from SQL Server, as well as performing data migration and transformation from Access/Excel sheets using SQL Server SSIS.

Proficient in dimensional data modelling for Data Mart design, identifying facts and dimensions, and developing fact tables and dimension tables using Slowly Changing Dimensions (SCD) techniques.

Skilled in error and event handling techniques, such as precedence constraints, breakpoints, check points, and logging, ensuring reliable and robust ETL processes.

Experienced in building cubes and dimensions with different architectures and data sources for business intelligence purposes, including writing MDX scripting.

Proficient in developing SSAS cubes, implementing aggregations, defining KPIs (Key Performance Indicators),

Contact this candidate