3–7 yrsMumbaiOn-siteDirect career pageEst. 12–22 LPA
Posted 11h ago
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Job description
Structured overview of role & requirements
About This Role
- Develop and implement scalable, secure, and cost-effective cloud-based data solutions using AWS and Databricks technologies.
- Design, build, and maintain ETL pipelines to extract, transform, and load data into data warehouses or lakes ensuring data quality and efficient data retrieval.
- Collaborate closely with analytics teams and stakeholders to support business analytics agenda and deliver actionable insights for long-term business performance.
Minimum Requirements
- 3 to 7 years of experience in data engineering with demonstrated expertise in Python, PySpark, SQL, Databricks, and AWS Cloud services (S3, Lambda, EC2, Glue, Redshift).
- Experience with ETL & Integration tools such as Databricks Lakeflow Connect and DataSphere; understanding of data warehousing concepts including SCD Types, Star Schema, Dimensional modeling.
- Notice period of 0-3 months allowed.
- No relocation support available; location requirement or onsite mandate: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Proven ability to design and optimize end-to-end data pipelines following best practices like Medallion Architecture (Bronze, Silver, Gold) on cloud platforms.
- Experience working collaboratively within delivery teams to meet project deadlines and adhere to data engineering standards and guidelines.
- Familiarity with GenAI tools (Genie, Kiro, Gemini) and scheduling technologies (Lakeflow Jobs, Airflow) indicates adaptability to emerging technologies and evolving data environments.