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Tier-1 brand, popular mid-level data engineer title, metro location, and broad Databricks/PySpark requirements.
Databricks, PySpark, and SQL skills are widely transferable, though biotech data governance adds moderate domain bias.
Explicit 5–8 years requirement plus mandatory Databricks/PySpark and cloud skills increases shortlisting strictness.
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Design, build, maintain, and analyze large-scale data solutions and pipelines using big data technologies like Databricks and Apache Spark to support business decisions.
Own end-to-end data pipeline projects including design, development, deployment, and performance optimization with focus on data quality, security, and governance.
Collaborate across global and cross-functional teams to integrate multiple data sources and deliver scalable, reliable data solutions leveraging cloud platforms (AWS preferred).
Bachelor’s or Master’s degree in Computer Science, IT, or related field.
5 to 8 years of experience in data engineering or related roles.
Hands-on experience with Databricks, Apache Spark (PySpark, SparkSQL), SQL, and big data processing including ETL pipeline development and tuning.
Willingness to work later shifts including evening or night shifts as required.
Proven ability to independently manage data pipeline projects end-to-end in fast-paced environments.
Strong expertise in big data technologies, data governance, and cloud platforms, especially AWS and Databricks.
Experience working in global, distributed teams across multiple time zones to deliver complex data engineering solutions.