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Job Description
Structured overview of role & requirementsAbout This Role
Design and build scalable, secure, cost-effective cloud-based data solutions using AWS and Databricks.
Develop and maintain ETL data pipelines to extract, transform, and load data into data warehouses/lakes while ensuring data quality and optimal retrieval performance.
Collaborate closely with analytics teams and stakeholders to support business analytics agenda and deliver insights using models/patterns.
Minimum Requirements
2 to 4 years of experience in data engineering with proficiency in Python, PySpark, SQL, Databricks, AWS services, and data modeling concepts (SCD, star schema).
Experience with cloud data solutions including AWS S3, Lambda, EC2, Glue, Redshift and Databricks Medallion Architecture.
Familiarity with scheduling tools like Lakeflow Jobs or Airflow; some exposure to GenAI tools (Genie/Kiro/Gemini) is mentioned.
Work Experience Required: 2 to 4 years in data engineering. No relocation support available.
Ideal Candidate Profile
Candidate should operate effectively in cloud-based data engineering environments leveraging modern data stacks and architectural patterns (Medallion Architecture).
Strong alignment with cross-functional collaboration involving Engineering Leads, Data Product Managers, and Analysts for delivering data solutions on time adhering to data engineering guidelines.
Demonstrated ability in building and optimizing ETL pipelines and ensuring data accuracy, suited to fast-moving analytics and modeling teams.