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Tier-1 brand, popular mid-level data role, metro location, and broad skillset create high applicant competition.
Core data-engineering skills are transferable, but biotech/regulatory preference increases industry specificity moderately.
Explicit 5–9 years requirement and mandatory Databricks/Spark/Python/cloud skills make shortlisting highly strict.
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Design, develop, test, and maintain scalable production data pipelines and ETL/ELT solutions for structured, semi-structured, and unstructured data.
Collaborate with data architects, data scientists, product, and DevOps teams to deliver high-quality data solutions and support data governance, security, and compliance controls.
Take ownership of end-to-end data engineering work including performance tuning, documentation, operational support, and continuous improvement of data platform processes.
5–9 years of relevant professional experience in data engineering or related fields.
Hands-on experience with Databricks, Apache Spark, PySpark, Spark SQL, Python, SQL, and cloud platforms (AWS or equivalent).
Experience designing, developing, and supporting production ETL/ELT pipelines with workflow orchestration and scheduled workloads.
Bachelor’s or Master’s degree in Computer Science, IT, Engineering, Data Science, or related field.
Experienced in handling large, complex datasets using Databricks and Spark with strong skills in performance tuning and data modeling.
Familiar with regulated industries such as biotechnology, pharmaceutical, or life sciences, and understands data privacy, security, and compliance requirements.
Operates well in Agile environments collaborating with cross-functional and global teams, owning data engineering deliverables from development through production support.