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Broad skillset, metro location, and common data-engineering role yield medium competition.
Data warehousing and ETL skills are highly transferable across industries.
Multiple mandatory data warehouse, cloud, ETL, and tooling requirements increase shortlisting strictness.
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Design and architect end-to-end data warehouse solutions including data models and OLAP structures.
Create and manage ETL pipelines to ingest data from structured and semi-structured sources, ensuring data quality, governance, and compliance.
Optimize data storage and query performance; develop data strategy aligned with business objectives; mentor data engineering teams on best practices.
Strong experience with data warehousing concepts, architecture, and dimensional modeling.
Proficiency in SQL and performance tuning; hands-on experience with cloud platforms, preferably Azure.
Experience with ETL tools such as SSIS, Informatica, Talend; knowledge of SQL Server, Snowflake, or Oracle databases.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in cloud data platforms, preferably Azure stack including Data Bricks, Data Factory, Synapse Analytics, and related tools.
Skilled in modern data architecture, including data lakes, data marts, and use of DevOps and CI/CD for data pipelines.
Able to guide and mentor teams, translate business requirements into scalable data solutions, and optimize analytics workloads.