4–6 yrsMumbaiOn-siteDirect career pageEst. 12–20 LPA
Posted 1d ago
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Job description
Structured overview of role & requirements
About This Role
- Design and build scalable, secure, cloud-based data solutions and pipelines to support business analytics and uncover trends for long-term performance.
- Develop, maintain, and optimize data pipelines ensuring data quality, accuracy, and efficient storage and retrieval within cloud environments.
- Collaborate closely with analytics teams and stakeholders to deliver analytics agenda using data engineering best practices and tools.
Minimum Requirements
- 4 to 6 years of experience in data engineering with expertise in Python, PySpark, SQL and cloud technologies (AWS: S3, Lambda, EC2, Glue, Redshift).
- Experience with Databricks platforms including LDP, Lakeflow Connect, Notebooks; knowledge of data warehousing concepts (SCD Types, Fact & Dimensions, Star Schema).
- Familiarity with data modeling techniques such as dimensional modeling; exposure to Medallion Architecture (Bronze, Silver, Gold).
- Work Experience Required: 4 to 6 years in Data Engineering. Notice period: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Experienced data engineer with a strong track record implementing and managing cloud-based ETL pipelines in enterprise environments.
- Technical proficiency in Python, PySpark, SQL, and AWS cloud stack combined with understanding of data warehousing and pipeline optimization.
- Ability to collaborate with analytic leaders and product teams within large organizations focused on data-driven decision making.