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Tier-1 brand, mid-level Data Engineer, metro location and broad Databricks/PySpark requirements increase applicant competition.
Core data engineering skills are broadly transferable across industries though some biotech domain knowledge helps.
Explicit 5–8 years plus mandatory Databricks, PySpark, cloud and ETL skills make screening highly strict.
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Design, develop, and maintain data pipelines and ETL processes for large-scale data integration and processing.
Take ownership of data pipeline projects including scope management, timelines, risk mitigation, and deployment.
Collaborate with cross-functional global teams to meet business data requirements and implement scalable data solutions on cloud platforms (AWS preferred).
5 to 8 years of experience in Computer Science, IT or related field with hands-on big data technologies like Databricks and Apache Spark (PySpark, SparkSQL).
Proficiency in SQL and experience with data visualization tools.
Bachelor’s or Master’s degree in Computer Science, IT or related field.
Willingness to work evening or night shifts as per business requirements.
Experienced in designing end-to-end data pipelines, ETL/ELT processes, and data integration for both structured and unstructured datasets.
Skilled in performance tuning of big data processing including Spark job tuning and optimization techniques.
Capable of collaborating effectively across global, cross-time-zone teams to deliver complex data solutions.