Data Engineer

Roles and Responsibilities

• Build, optimize, and manage data ingestion workflows from multiple structured and unstructured data sources.
• Develop and optimize SQL queries, stored procedures, functions, and views for high-volume relational databases.
• Design and maintain data models that support analytics, reporting, and data products.
• Design, develop, and maintain scalable data pipelines using SQL, Python, and modern ETL/ELT frameworks.
• Perform data profiling, cleansing, validation, and quality monitoring to ensure data accuracy, integrity, and consistency.
• Optimize database and pipeline performance through indexing, partitioning, query tuning, and workload optimization.
• Collaborate with Product Managers, and business stakeholders to understand business requirements and deliver analytics-ready data solutions.
• Support the development and enhancement of data products by delivering reliable, scalable, and governed datasets.
• Document data pipelines, data models, technical designs, and operational processes in accordance with data governance standards.
• Implement best practices for data security, governance, metadata management, and compliance.
• Contribute to automation, reusable frameworks, and continuous improvement initiatives.

Qualifications & Experience

• Bachelor's degree in computer science, Information Technology, and Engineering.
• 4--10+ years of strong hands-on experience in SQL development, ETL, and BI/reporting.

Required Skills & Experience

• Strong proficiency in SQL (T-SQL / PL-SQL) with advanced query writing and performance tuning.
• Hands-on experience with ETL/ELT tools such as SSIS, Azure Data Factory, Fivetran, AWS Glue
• Experience with Azure Data Services, such as: Azure Data Factory, Azure Synapse Azure Data Lake Storage (ADLS), Azure SQL Database, Microsoft Fabric.
• Hands-on experience with AWS Data Services including AWS Lambda, S3, Athena, Lake Formation & DataZone.
• Strong programming skills in Python for data engineering and automation.
• Strong data analysis skills, including data exploration, profiling, validation, KPI development, and translating business requirements into actionable insights.
• Solid understanding of data warehousing concepts, star/snowflake schemas, and dimensional modeling.
• Experience supporting or developing data products and understanding the end-to-end data product lifecycle.

Preferred Skills:

• Familiarity with version control (Git) and CI/CD for data pipelines.
• Exposure to Power BI, Service administration, gateways, and workspace management.
• Understanding of data privacy, retention, and governance frameworks.