Data Engineer
2 days ago
We are looking for a highly skilled Data Engineer with expertise in PySpark and Databricks to design, build, and optimize scalable data pipelines for processing massive datasets.
IMMEDIATE JOINERS PREFERRED
Responsibilities
Build & Optimize Pipelines: Develop high-throughput ETL workflows using PySpark on Databricks.
Data Architecture & Engineering: Work on distributed computing solutions, optimize Spark jobs, and build efficient data models.
Performance & Cost Optimization: Fine-tune Spark configurations, optimize Databricks clusters, and reduce compute/storage costs.
Collaboration: Work closely with Data Scientists, Analysts, and DevOps teams to ensure data reliability.
ETL & Data Warehousing: Implement scalable ETL processes for structured & unstructured data.
Monitoring & Automation: Implement logging, monitoring, and alerting mechanisms for data pipeline health and fault tolerance.
Qualifications
Bachelor's/Master's in Computer Science, Data Engineering, or a related field.
3+ years of experience in Big Data Engineering with PySpark & Databricks.
Strong understanding of distributed computing, partitioning, and Spark internals(RDDs, DAGs, Catalyst Optimizer).
Expertise in SQL & NoSQL databases for structured & semi-structured data.
Proficiency in Python and Linux-based environments.
Experience with Kafka (or other streaming platforms) and HealthCare Data [Optional].
Familiarity with AWS/GCP/Azure and storage solutions (S3, Delta Lake, HDFS).
- Python | PySpark | Databricks
- SQL | Data Warehousing | Delta Lake
- Linux | Shell Scripting
- Kafka (Optional)
- Cloud Platforms (AWS/GCP/Azure)
Join us and build the future of data engineering
Job Types: Full-time, Permanent, Contractual / Temporary
Work Location: In person
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