Posted:18 hours ago|
Platform:
On-site
Full Time
Job Responsibilities:
✅Build and optimize scalable data pipelines using Python, PySpark, and SQL.
✅Design and develop on AWS stack (S3, Glue, EMR, Athena, Redshift, Lambda).
✅Leverage Databricks for data engineering workflows and orchestration.
✅Implement ETL/ELT processes with strong data modeling (Star/Snowflake schemas).
✅Work on job orchestration using Airflow, Databricks Jobs, or AWS Step Functions.
✅Collaborate with agile, cross-functional teams to deliver reliable data solutions.
✅Troubleshoot and optimize large-scale distributed data environments.
Must-Have:
✅4–6+ years in Data Engineering.
✅Hands-on experience in Python, SQL, PySpark, and AWS services.
✅Solid Databricks expertise.
✅Experience with DevOps tools: Git, Jenkins, GitHub Actions.
✅Understanding of data lake/lakehouse/warehouse architectures.
Good to Have:
✅AWS/Databricks certifications.
✅Experience with data observability tools (Monte Carlo, Datadog).
✅Exposure to regulated domains like Healthcare or Finance.
✅Familiarity with streaming tools (Kafka, Kinesis, Spark Streaming).
✅Knowledge of modern data concepts (Data Mesh, Data Fabric).
✅Experience with visualization tools: Power BI, Tableau, QuickSight.
Sparshcorp
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