Posted:1 week ago|
Platform:
Work from Office
Full Time
• Develop and maintain scalable data pipelines using Apache Spark on Databricks.
• Build end-to-end ETL/ELT pipelines on AWS using services like S3, Glue, Lambda, EMR, and Step Functions.
• Collaborate with data scientists, analysts, and business stakeholders to deliver high-quality data solutions.
• Design and implement data models, schemas, and lakehouse architecture in Databricks.
• Optimize and tune Spark jobs for performance and cost-efficiency.
• Integrate data from multiple structured and unstructured data sources.
• Monitor and manage data workflows, ensuring data quality, consistency, and security.
• Follow best practices in CI/CD, code versioning (Git), and DevOps practices for data applications.
• Write clean, reusable, well-documented code using Python / PySpark / Scala.
• 4-8 years of total experience in data engineering or big data development.
• 23 years hands-on experience with Databricks and Apache Spark.
• Proficient in AWS cloud services (S3, Glue, Lambda, EMR, Redshift, CloudWatch, IAM).
• Strong programming skills in PySpark, Python, and optionally Scala.
• Solid understanding of data lakes, lakehouses, and Delta Lake concepts.
• Experience in SQL development and performance tuning.
• Familiarity with Airflow, dbt, or similar orchestration tools is a plus.
• Experience in CI/CD tools like Jenkins, GitHub Actions, or Code Pipeline.
• Knowledge of data security, governance, and compliance frameworks.
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