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Popular mid-senior Big Data role in Pune with broad Spark/Databricks/AWS requirements increases competition.
Data engineering skills are broadly transferable, but Databricks/AWS specialization increases industry fit sensitivity.
Explicit 6–8 years plus mandatory Spark, Databricks, AWS, and JVM/Python skills make filters stringent.
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Design, develop, and maintain scalable batch and streaming data pipelines for large-scale structured datasets.
Ensure data quality, reliability, scalability, and availability of data platform components, owning end-to-end delivery and support.
Troubleshoot performance bottlenecks and collaborate with cross-functional teams to deliver robust data engineering solutions, mentoring junior team members.
6 to 8 years of hands-on experience in Big Data or Data Engineering.
Proficiency in Java or Scala (preferred), strong Python experience also acceptable.
Experience with Apache Spark, Apache Airflow (or equivalent), and AWS services including S3, Glue, and EMR.
Location: Pune, India; Work Experience Required: 6 to 8 years; Notice period: Not explicitly mentioned in the JD.
Experienced individual contributor comfortable owning and optimizing data platform components in a fast-paced environment.
Strong technical expertise in distributed data processing frameworks, cloud-native data engineering (AWS), and modern data platform architectures including Data Lake and Lakehouse.
Skilled in writing clean, maintainable, and scalable code with exposure to AI-assisted coding tools and mentoring junior engineers.