Data Engineer - ETL/ELT, Cloud, & Analytics Expert

CONTACT

Address: London UK

Phone: +44-739*******

Email:

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

TECHNICAL SKILLS

Programming & Querying: Python,

SQL, Excel

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Data Analysis & Libraries: Pandas,

NumPy, Scikit-learn, Stats models

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Visualization & Reporting: Matplotlib,

Seaborn, Plotly, Power BI, Dashboard

Design for Non-IT Stakeholders

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Machine Learning & Forecasting:

Supervised & Unsupervised Learning,

Time Series Forecasting, Model

Evaluation, Hyperparameter Tuning

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Cloud & Data Platforms: AWS (EC2,

S3, IAM), Snowflake (Data

Warehousing, SQL queries, Role

Management), Azure Data Factory

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Workflow & Tools: Git, GitHub, Google

Colab, MS Office 365

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• Analytical, detail-oriented

• Strong communication and teamwork

• Solution-focused problem solving

• Adaptable time management

• Critical thinking under pressure

PROFESSIONAL SUMMARY

Experienced Data Engineer with 4+ years of expertise in designing and optimizing ETL/ELT pipelines, data lakes, and data warehouses across Azure, AWS, GCP, and Snowflake. Skilled in Azure Data Factory, Synapse Analytics, Databricks, Spark (Scala/Pyspark), SQL/T-SQL, and Python, with strong experience in data automation and CI/CD (Azure DevOps, Jenkins). Adept at building scalable, secure, and high-performance data platforms to support financial analysis, risk assessment, and business intelligence. Proficient in real-time data processing, pipeline optimization, and cross-functional collaboration to deliver reliable data solutions in Agile environments. CERTIFICATIONS/PROFESSIONAL DEVELOPMENT

COURSES

• Foundations: Data, Data, Everywhere – Google (Coursera)

• 2025

Covered data lifecycle, analysis process, data-driven decision-making, and analytical thinking.

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Intro to Snowflake for Developers, Data Scientists & Data Engineers – Snowflake (Coursera)

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Hands-on experience with creating/managing warehouses, schemas, tables, using features like Time Travel, UDFs, Stored Procedures, Snowpark, and working with DataFrames.

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PROFESSIONAL EXPERIENCE

Data Engineering, 03/2023 to current

Virtusa

Description: - As a requirement of my work, I have to conduct financial analysis, valuation, and risk assessment, to support the team in making informed decisions for clients. Trained in various financial tools and software to analyze the impact on market and client securities and act accordingly.

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• Responsibilities: -

Followed the SDLC process, including requirements gathering, design, development, testing, deployment, and maintenance.

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Have good experience working with Azure Blob and Azure data lake storage and loading data into Azure SQL Synapse Analytics (DW).

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Worked on creating a Data Lake Analytics account and creating a Data Lake Analytics Job in Azure Portal using SQL Script

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Extract, Transform, and Load data from source systems to Azure Data Storage services using a combination of Azure Data Factory, T-SQL, Spark SQL, and U-SQL Azure Data Lake Analytics.

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Utilized version control systems like Git and source code management best practices for collaborative development.

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RAJASEKHAR MUNAGALA

Cloud Engineer (Data Engineering), 02/2022 to 02/2023 Tata Consultancy Services (TCS)

Collaborated closely with cross-functional teams, including data scientists, data analysts, and business stakeholders, ensuring alignment with data requirements and delivering scalable and reliable data solutions.

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Used Azure DevOps for CI/CD (Continuous Integration and Continuous deployment) and Azure repos for version controlling.

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Developed ETL pipelines in and out of the Data Warehouse using a combination of Python and Snow SQL.

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Implemented Big Query data processing in Big Query on the GCP Pub/Sub theme, using Python's cloud data streams and using Python's Rest API to load data into Big Query from other systems.

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Developed and maintained efficient ETL/ELT processes using SQL and T-SQL scripts to transform and cleanse data in Azure Synapse Analytics.

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Worked on Python scripting to automate the generation of scripts. Data curation done using Azure Data Bricks.

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Wrote Scala code for Spark applications, as Scala is the native language for Spark and provides better performance for certain operations.

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Worked with Azure DevOps for continuous integration, delivery, and deployment of Spark applications.

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Created several Data Bricks Spark jobs with PySpark to perform several tables to table operations.

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Developed UNIX scripts to automate different tasks involved as part of the loading process and worked on Power BI software for reporting needs.

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Managed and tracked all project documentation through JIRA and Confluence software development tools.

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• Experienced in Agile methodology involved in bi-weekly sprints, daily

• Scrum meetings with backlogs and story points.

Environment: Azure Blob, Azure data lake, Azure SQL Synapse Analytics (DW), Data Bricks, Spark, Scala, Data Lake Analytics, Azure Data Factory, T- SQL, Spark SQL, Data Analysts, Azure CI/CD, ETL, Python, Snow SQL, Rest API, TL, ELT, SQL, T SQL, Unix, Power BI, Jira, Confluence, Agile, Scrum

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• Client: Fidelity National Information Services (FIS)

• Description:

Worked as part of the Data Engineering and Platform Support team for Fidelity National Information Services (FIS), providing end-to-end support for financial data pipelines and reporting systems. The project involved designing, automating, and monitoring data workflows on AWS and Snowflake to ensure timely availability of accurate financial data for multiple business units including risk, compliance, and operations.

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Utilized Excel, SQL, and regression models to analyze large financial datasets, identify trends, and generate actionable insights for stakeholders.

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Designed and supported automated data pipelines on AWS and Snowflake, ensuring accurate and timely availability of business-critical data for analysis and reporting.

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Built queries and dashboards to track data quality, system performance, and SLA metrics, enabling business teams to make data-driven decisions.

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Conducted root cause analysis on data and pipeline issues, using structured problem-solving techniques to reduce recurring incidents by 35% within a year.

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Facilitated weekly cross-functional reviews with business and vendor teams, presenting data-driven reports and KPIs, which led to a 30% increase in user satisfaction.

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• Executed ITIL-aligned workflows (incident, change, problem management), Data Platform Support Engineer, 06/2020 to 01/2022 Skylives Info Tech Pvt Ltd - Hyderabad

improving reporting transparency and reducing ticket resolution time by 15%.

Environment/Tools: AWS, Snowflake, SQL, Excel, Python, Regression Models, ITIL Workflows, ServiceNow, Confluence, Data Visualization & Reporting Dashboards.

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Description:- This project involved development of warehouse for Client business. It involves extracting data from Legacy applications, creating text extracts and loading them to staging. Data was further cleaned and loaded in corresponding dimensions and facts. The warehouse incremental load continues still and business extensively using it for data analysis &reporting.

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• Responsibilities:-

Developed a data pipeline and used Azure stack components such as Azure Data Factory, Azure Data Lake, Azure Data Bricks, Azure Synapse analytics, and Azure Key Vault for analytics.

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Developed strategies for handling large datasets using partitions, Spark SQL, broadcast joins and performance tuning.

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Extract Transform and Load [ETL] data from Source Systems to Azure Data Storage services using a combination of Azure Data Factory, T-SQL, Spark SQL, and Azure Data Lake Analytics then running scripts in Data Bricks.

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Created data ingestion systems to pull data from traditional RDBMS platforms and store it in NoSQL databases such as MongoDB.

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Involved in loading data from the Linuxlife system to Hadoop Distributed File System (HDFS) and setting up HIVE, PIG, HBASE, and SQOOP on Linux/Solaris Operating System.

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Developed and enhanced Snowflake tables, views, and schemas to enable effective data retrieval and storage for reporting and analytics requirements.

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Optimized Python code and SQL queries, created tables/views, and wrote custom queries and Hive-based exception processes.

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Created Hive tables as per requirements, internal or external tables defined with appropriate static and dynamic partitions, intended for efficiency.

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Implemented continuous integration and deployment (CI/CD) pipelines through Jenkins to automate Hadoop job deployment and managed Hadoop clusters with Cloudera.

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Built streaming ETL pipelines using Spark streaming to extract data from various sources, transform it in real-time, and load it into a data warehouse Snowflake.

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Designed and built Spark/PySpark-based Extract Transformation Loading

(ETL) pipelines for migration of credit card transactions, account, and customer data into enterprise Hadoop Data Lake

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Writing complex PL/SQL queries and procedures to extract, transform, and load data from various sources, ensuring data accuracy and completeness.

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Used Spark and Spark-SQL to read the parquet data and create the tables in hive using the Scala API.

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• Utilized JIRA to manage project issues and workflow. Exposed to all aspects of software development life cycle (SDLC) like Analysis, Planning, Developing, Testing, implementing and post- production analysis of the projects. Worked through Waterfall, Scrum/Agile Methodologies.

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• Created UNIX shell scripts to load data from flat files into Oracle tables. Environment: Spark, Spark SQL, PL SQL, HDFS, Kafka, Sqoop, Waterfall, Scrum, Agile, Snowflake, CI/CD, ETL, Cloudera, Linux, NO SQL, T SQL, Mongo DB.

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Contact this candidate