Description

Technology Stack: Python, SQL, PySpark, Apache Spark, Azure Data Factory, Databricks, Azure Data Lake, AWS, Snowflake, ETL/ELT, SQL Server, Power BI, Git.

Roles and Responsibilities
Designed, developed, and maintained scalable data pipelines to extract, transform, and load data from multiple sources.

Developed ETL/ELT workflows using Python, SQL, PySpark, and Azure Data Factory.

Built and optimized data processing solutions using Apache Spark and Databricks.

Worked with structured and unstructured data from relational databases, APIs, flat files, and cloud storage.

Designed and implemented data integration solutions to support business intelligence and analytics requirements.

Developed complex SQL queries, stored procedures, views, and functions to transform and process large datasets.

Implemented data validation, data cleansing, data transformation, and data quality checks to ensure data accuracy.

Worked with Azure Data Lake Storage and cloud-based data platforms to store and manage large volumes of data.

Created and maintained data warehouses, data marts, and dimensional data models.

Integrated data from multiple enterprise systems using REST APIs and database connectors.

Monitored data pipelines, identified failures, and resolved performance and data processing issues.

Implemented incremental data loading, partitioning, and optimization techniques to improve pipeline performance.

Worked with Snowflake for data loading, transformation, query optimization, and warehouse management.

Implemented data security, access controls, and governance practices to protect sensitive business information.

Used Git and Azure DevOps for source control, CI/CD, and deployment management.

Collaborated with data analysts, data scientists, and business teams to understand data requirements.

Participated in Agile/Scrum ceremonies, technical discussions, code reviews, and production support.

Technical Skills
Programming: Python, SQL, PySpark

Big Data: Apache Spark, Hadoop, Databricks

ETL Tools: Azure Data Factory, Informatica, SSIS

Cloud Platforms: Microsoft Azure, AWS

Data Storage: Azure Data Lake, Amazon S3

Data Warehousing: Snowflake, Azure Synapse Analytics, SQL Server

Databases: PostgreSQL, MySQL, Oracle, SQL Server

Data Modeling: Star Schema, Snowflake Schema, Dimensional Modeling

DevOps: Git, Azure DevOps, CI/CD, Jenkins

Reporting: Power BI

Methodologies: Agile, Scrum, SDLC

Qualifications
Bachelor's degree in Computer Science, Information Technology, or a related field.

Experience in data engineering, ETL development, and data pipeline implementation.

Strong programming knowledge in Python, SQL, and PySpark.

Experience working with cloud platforms and data warehousing technologies.

Understanding of data modeling, database concepts, and data integration.

Strong analytical, problem-solving, debugging, and communication skills

Key Skills

Education

Any Graduate