Power Bi Data Engineer

Location:

Atlanta, GA, 30303

Posted:

August 04, 2025

Contact this candidate

Resume:

Srilekha Jidagam

Senior Associate II, Data Engineer

(Python, SQL, Power BI, Azure, AI/ML)

**********@*****.***

+1-943-***-****

Highly accomplished Data Engineer with more than 7 years of experience architecting and implementing end-to-end data solutions. Expertise in cloud platforms (preferably Azure, AWS) for scalable systems and automated ETL pipelines. Power BI & Fabric Certified and proficient in Kibana, Tableau, Cognos and scripting languages like python, SQL for impactful data visualization, driving enhanced data accessibility and informed decision-making. Proven ability to collaborate with cross-functional teams to build robust, high-integrity data pipelines. Experienced in integrating AI/ML for advanced predictive analytics and business performance optimization. Passionate about continuous technical advancement and delivering innovative data workflows.

●Led the design, development, and optimization of end-to-end data pipelines, processing terabytes of both real-time and batch data, resulting in a 60% reduction in data processing time, enhancing reporting efficiency.

●Developed AI-driven data pipelines using Python, SQL, PySpark, and cloud platforms (Azure, AWS, GCP), enhancing data retrieval efficiency by 40% and enabling real-time analytics.

●Analyzed large datasets related to different streams of data like ITSM Data, Healthcare, Banking, Network, Backup & Recovery, Finance Services, Performance and capacity, Observability developing actionable insights to enhance business and ensured data integrity and quality by implementing automated validation and monitoring processes, achieving a 98% accuracy rate in reporting.

●Optimized SQL query performance, reducing load times by 50% and significantly improving overall system efficiency, ensuring faster and more reliable reports.

●Built AI-powered reporting solutions with Power BI Smart Narrative and Q&A (NLP – Natural Language Processing), automating insights generation and reducing decision-making time by 30%.

●Engineered predictive models (e.g., Random Forest, ARIMA) for demand forecasting, improving decision-making and increasing business efficiency by 25%.

●Designed and implemented data pipelines for real-time streaming and batch data processing, using Azure Data Factory, Azure Databricks, and AWS services (e.g., SageMaker, Redshift), enabling the use of AI models for continuous data flow, improving data quality and analytics.

●Orchestrated data flows from APIs, web logs, and other data sources, transforming raw data into formats suitable for machine learning analysis and AI model training to automate anomaly detection, reducing data errors by 25%.

●Demonstrated strong problem-solving skills, by effectively troubleshooting complex data issues, collaborating with cross-functional teams, and utilizing tools like Jira to manage project timelines and ensure timely deliverables within agile teams.

●Optimized data pipelines to ensure scalability, integrity, and seamless integration with other business systems and developed decision-driven insights through interactive dashboards using reporting tools like Power BI/ Fabric, Kibana, Grafana, Tableau, and Cognos, presenting data to senior management, influencing key business decisions, and enabling stakeholders to make informed, data-backed choices.

●Led the Incident Management process, ensuring timely logging, prioritization, and resolution of incidents in line with SLAs and business objectives, minimizing disruptions to reporting services.

●Supported Problem and Change Management by identifying recurring issues, escalating them for prompt resolution, and ensuring continuous improvement in reporting processes.

●Championed the adoption of new technologies, including AI and machine learning models in CI/CD pipelines, continuously improving the performance of data pipelines and reporting solutions.

●Built AI-powered reporting solutions with Power BI Smart Narrative and Q&A (NLP – Natural Language Processing), automating insights generation and reducing decision-making time by 30%.

●Extensive experience in SQL/PL-SQL, including developing stored procedures, packages, triggers, table partitioning, dynamic SQL, collections, and performance tuning.

●Mentored junior engineers in data engineering methodologies, technologies, and AI best practices, fostering machine learning model development, deployment, and AI-powered data solutions.

●Automated repetitive tasks and processes through scripting, saving time and reducing errors.

●Managed backup and disaster recovery plans, ensuring minimal data loss during unforeseen events.

Data Visualization

Microsoft’s Power BI Desktop,Microsoft’s Power BI Service, Kibana, Tableau, AWS QuickSight, IBM Cognos, Custom Visuals, Power BI Q&A

Scripting

Python (Pandas, NumPy, Matplotlib & Seaborn, scikit-learn), SQL, KQL,DAX(Data Analysis Expression), PySpark, C, C++, Scala, Power/M Query, Shell Scripting,PL/SQL,T-SQL.

Database Management

MySQL/NoSQL, MS SQL Server, Azure SQL Database, AmazonDynamoDB, Cassandra, BigQuery, Elasticsearch, MongoDB, DB2visualizer, IBM Cognos Framework Manager (Basic), IBM DB2

Datawarehouse

HDFS, Azure Synapse SQL Analytics, Azure Dedicated SQL Pool, Blob Services, Amazon Redshift.

Big Data Eco-System

Apache Spark, Hadoop, YARN, Kafka, MapReduce, Oozie, Pig, Flume, Predictive Analytics, Data Mining, Data Pipelines, Data Warehousing, Data Lake, Storage Accounts, Azure Data Factory, SSIS (SQL Server Integration Services)

Cloud & Streaming Platforms

Azure, IBM Cloud, AWS, GCP, Kafka, Azure Databricks, Microsoft Fabric

Job Scheduler

Apache Airflow, IWS Scheduling, Cron Jobs, Power Automate

Generative AI

Text Generation, Predictive Analytics, Machine Learning Models, NLP

Operating Systems

Windows, Unix, Linux, RHEL, Redshift

IDE Tools & Utilities

Spyder, Jupyter Notebooks, Visual Studio Code, Eclipse, PyCharm

Ticketing Tools

ServiceNow, SRM, Science Logic

Version Control

Git, SharePoint

Web Technologies

JSON, XML, HTML, CSS

Microsoft Tools

Excel, Powerpoint, outlook, Teams, Docs, Ms Word

Others

ETL (Extract Transform Load) Process, Machine Learning, Forecasting Analytics, Data Security, Data Mining, Data Modelling, Data Analysis, Sentiment Analysis, CI/CD pipelines.

●Databricks Data Engineer Associate – July 23,2025

●Microsoft Certified Fabric Analytics Engineer Associate (DP-600) – May 21,2025

●Microsoft Certified Power BI Data Analyst Associate (PL-300) - June 5, 2024

●Azure AI-900 (Microsoft Azure AI Fundamentals) - January 4, 2024

●Azure Fundamentals: DP-900 (Microsoft Azure Data Fundamentals) - February 2, 2024

●Microsoft Certified: Azure Fundamentals (AZ-900) - January 4, 2023

●Data Analysis with Python (Coursera) - September 4, 2020

●Python for Data Science (IBM) - August 1, 2019

●Best Values & Practitioner Award - 2020 for outstanding commitment to code optimization, innovation, and on-time project deliverables

●Project Excellence Award - 2022 for the successful Transport for New South Wales (TfNSW) project, recognized for refining certifications and delivering high-quality results

●Certification of Achievement – 2023

Senior Associate II, Data Engineer Kyndryl Nov,2021 – Present

Project – 1

Client : Scholastic, New York, NY

Role : Sr.Data Engineer

Key Contributions:

●Developed and optimized SQL queries to extract and transform data from relational databases/ Lakehouse into enterprise version of Power BI, ensuring seamless reporting and analytics for business stakeholders.

●Designed and implemented end-to-end ETL pipelines using Azure Data Factory and Apache Spark to process large-scale datasets, facilitating efficient data transformation and loading into Azure Synapse for advanced analytics.

●Created and maintained complex DAX (Data Analysis Expressions) and Power queries (M) in Power BI to build custom calculations, enhance data models, and support dynamic, actionable reporting.

●Worked on dimensional data modeling using Star schema in Power BI, optimizing data structures for efficient reporting and analysis.

●Developed and launched a comprehensive Network Performance Dashboard in Power BI, delivering real-time metrics and insights for network optimization and proactive issue identification.

●Implemented both star and snowflake schema models, ensuring efficient querying and reporting for Power BI and other business intelligence tools.

●Identified manual, time-consuming workflows and developed automated solutions using the Power Platform, including Power Apps for user interfaces and Power Automate for process orchestration.

●Developed customized network-related visualizations in Power BI, identifying trends and patterns in network traffic, latency, and availability to support strategic planning and informed decision-making.

●Produced and maintained detailed documentation to help stakeholders effectively use Power BI dashboards and reports.

●Analyzed performance and generated client-specific reports using AWS QuickSight and Power BI, providing actionable insights based on detailed client queries.

●Integrated and unified data from SolarWinds and ServiceNow, streamlining analysis and ensuring accurate, consistent data reporting across platforms.

●Enabled seamless data visualizations in Power BI, transforming complex statistics into clear, actionable graphs and charts for better decision-making and moved this reports to Fabric.

●Developed customized network-related visualizations in Power BI, identifying trends and patterns to support strategic planning and informed decision-making.

●Implemented Git branching strategies to effectively manage feature development, hotfixes, and release cycles, ensuring smooth version control and collaborative development.

Environment:

MySQL Database, SolarWinds, ServiceNow, SQL, Power/M Query, DAX (Data Analysis Expression), Microsoft Power BI, AWS QuickSight, Microsoft Fabric, Network Metrics, Azure, Power BI Dataflow Gen1/Gen2 Scheduling, SharePoint, Power BI Q&A (Natural Processing Language), Smart Narrative, Git, Star, Snowflakes, Azure Data Factory, Apache Spark, Azure Synapse, csv, Excel, Ms Word, Powerpoint, Data Modelling, Data Warehouse, Network performance

Project – 2

Client : Co – Operators, Guelph, ON

Role : Sr. Data Engineer

Key Contributions:

●Extracted data from multiple sources, including SRM Tool via API calls and SharePoint Folders, using Python scripts to ensure smooth and efficient data integration.

● Analyzed large datasets (over 10GB) from the SRM tool, leveraging Python, spark notebooks for statistical analysis and developing new performance metrics.

● Developed actionable insights and visualized them through interactive dashboards, enhancing business intelligence and decision-making.

● Automated data updates by scheduling cron jobs via Apache Airflow, ensuring timely and accurate data delivery in alignment with client requirements.

● Diagnosed and analyzed server performance over time, identifying discrepancies in server counts and utilization categories (idle, overutilized, etc.), and improved data accuracy by removing irrelevant data from the Disk Dashboard.

● Migrated existing data pipelines from Logstash to GCP.

●Designed and implemented data ingestion, transformation, and curation functions on GCP using GCP native services and Python.

●Enhanced network observability by building custom Python scripts that collected and processed data from network devices, creating detailed visualizations in Grafana.

●Designed and optimized data models and schemas, such as Star Schema in Power BI, improving data retrieval times by 30%, enabling faster and more efficient analytics and reporting.

●Created and deployed applications on GCP using Data Proc, Dataflow, Composer, BigQuery, Bigtable, Cloud Storage, GCS, and various DAG operators.

●Implemented star and snowflake schema models to enable efficient querying and reporting for business intelligence tools like Power BI.

Environment:

SRM (Server Resource Management), SharePoint, Python, Logstash, PySpark, UNIX Shell Scripting,Git, KQL, Apache Elasticsearch, Kibana, Grafana, Microsoft Fabric, GCP, BigQuery, Apache Airflow, Power BI Desktop, Power BI service, csv, Excel, Ms Word, Powerpoint, ETL pipelines, Data Modeling, Data Staging.

Project – 3

Client : Medstar Health, Columbia, MD

Role : Sr. Data Engineer

Key Contributions:

●Designed and developed scalable Power BI dashboards and reports, transforming complex datasets

into actionable insights that drove sustainability-driven strategies and informed key business decisions

●Automated data extraction from the Science Logic tool to Outlook emails using Python scripts, optimizing data transfer and communication workflows.

●Performed data transformation and cleaning, ensuring high-quality data ingestion into Elasticsearch for generating actionable insights and visualizing them through interactive dashboards.

●Led the creation and maintenance of real-time reporting solutions, providing stakeholders with up-to-date insights to optimize operations, logistics, and sustainability initiatives.

●Worked closely with cross-functional teams, including business stakeholders, to understand data needs and translate them into scalable reporting solutions that aligned with company goals.

●Scheduled and automated Python script jobs in Apache Airflow, ensuring timely and accurate data updates in accordance with client requirements.

●Applied descriptive analytics using Business Intelligence tools like Tableau, Power Bi, integrating multiple data sources and creating customizable dashboards to deliver impactful storytelling.

●Implemented data governance practices, ensuring the accuracy and consistency of key business metrics, enhancing reporting quality and integrity.

●Presented key data-driven insights to senior management, influencing strategic decision-making and driving important business outcomes.

Environment:

Science Logic, SharePoint, Python, Logstash, Apache Elasticsearch, Kibana, Tableau, Apache Airflow, KQL, Azure Data Factory, Azure Synapse, Scheduled Reporting, Power BI Desktop, Power BI Service, Power Query, DAX, Azure SQL Database, SQL Server, IBM DB2, Excel, Ms Word

Project – 4

Client : Broadridge Financial Solutions, Owings Mills, MD

Role : Sr. Data Analyst

Key Contributions:

●Extracted and integrated data from various sources, such as SRM Tool via API calls and SharePoint Folders, using Python scripts to ensure smooth and efficient data flow.

●Analyzed large datasets related to performance and capacity, developing actionable insights and visualizing them through interactive dashboards to enhance business intelligence.

●Implemented performance tuning strategies, improving system throughput, reducing latency, and ensuring high availability of critical reporting systems.

●Presented data-driven insights to senior management, influencing key business decisions and strategic direction through effective Power BI dashboards and reports.

●Managed data scheduling and resources using Apache Airflow, ensuring efficient execution and timely project deliverables.

●Designed and implemented backup and disaster recovery strategies, minimizing data loss and ensuring business continuity in the event of unforeseen disruptions

●Contributed to version-controlled codebase using Git to manage and track changes in Python scripts and automation tools, ensuring smooth collaboration and version management across the team.

Environment:

SRM (Server Resource Management), SharePoint, Python, Logstash, PySpark, Apache Lucene, Apache Elasticsearch, Kibana, Tableau, Apache Airflow, IWS Scheduling, GIT, KQL, SQL, Data Transformation, Data Cleaning

Project – 5

Client : Kaiser Permanente, Oakland, CA

Role : Sr. Data Engineer

Key Contributions:

●Coordinated Change Management activities, ensuring that all changes were thoroughly documented, communicated effectively to impacted teams, and aligned with business objectives.

● Designed high-performing data warehouse systems, optimizing business intelligence and analytics.

● Designed and implemented a data pipeline using Python scripts to process and store refined data in Elasticsearch, ensuring efficient data flow and easy retrieval.

● Optimized existing data processing systems to enhance efficiency and reduce processing time significantly.

● Analyzed data and developed decision-driven insights through interactive dashboards, which were presented to senior management, influencing key business decisions and enabling stakeholders to make informed, data-backed choices.

● Designed and implemented data ingestion, transformation, and curation functions on GCP using GCP native services and Python.

● Optimized data pipelines for performance and cost efficiency in large-scale data lakes.

● Designed and automated BigQuery tables and Google Cloud Functions to enable reporting, analysis, and modeling.

Environment:

IBM Cloud, BigQuery, Python, Logstash, Elasticsearch, Kibana, IBM Cognos Analytics, GCP, Apache Airflow, Kafka, Business Intelligence, Data Anaytics, Data Cleansing, IBM DB2

Data Engineer Associate IBM Apr,2018 – Oct,2021

Project – 1

Client : Air Canada, Montreal, CA

Role : Data Engineer

Key Contributions:

●Extracted data from various sources, including ServiceNow and SRM tool, using Python-based API integrations to streamline data collection and processing.

●Analyzed, cleaned, and transformed raw data by creating calculated columns and loading it into the appropriate databases for further analysis and reporting.

●Developed data models and created packages in IBM Cognos Framework Manager, ensuring accurate mapping of required tables for seamless reporting.

●Implemented Big Data infrastructure, data modelling with complete end-to-end deployment process integrating different enterprise systems and migrated data from Oracle data warehouse to Elasticsearch using Python.

●Implemented forecasting models, including Random Forest Classifier and ARIMA, to predict trends and forecast incident occurrences and priorities, enabling data-driven decision-making.

●Created multiple interactive reports and data-driven insights in IBM Cognos Analytics Report Studio and Kibana, providing actionable intelligence for decision-making.

●Collaborated with various AWS services such as Glue, EMR, SNS, SQS, SageMaker, Bedrock, Lambda, EC2, RDS, and Athena to process data for downstream customers.

●Created libraries and SDKs to facilitate JDBC connections to Hive databases and query data using Play Framework and various AWS services.

●Implemented Spark scripts to accelerate data loading from Hive to Amazon RDS (Aurora).

●Developed Hive views for application usage through Spark SQL.

●Implemented data security measures using Apache Ranger, including row-level filters and group-level policies.

●Normalized data to meet business needs, involving data cleansing, datatype modifications, and various transformations using Spark, Scala, and AWS EMR.

Environment:

ServiceNow, SRM (Server Resource Management), Python, Logstash, PySpark, Scala, MySQL, PostgreSQL, Apache Lucene, Apache Elasticsearch, Kibana, IBM Cognos Analytics, Airflow, IWS

Scheduler, Amazon RDS, AWS, AWS Glue, AWS EMR, EMR, MapReduce, DB2 Visualizer, IBM Cognos Framework Manage, Predictive Analytics, Machine Learning Models.

Project – 2

Client : ICICI, Hyderabad, India