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Tier-1 brand and metro location increase competition, while seniority and niche Databricks skills moderate applicant density.
Core data engineering skills transfer across industries, though enterprise data architecture and biotech familiarity increase domain sensitivity.
Explicit 12-15 years plus mandatory Databricks/Spark/AWS and enterprise data architecture requirements make shortlisting highly strict.
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Own design, development, and optimization of complex data pipelines and frameworks for R&D domain using Databricks, Spark, and Delta Lake.
Drive adoption of emerging big data technologies and performance tuning to enhance scalability, data delivery, and platform observability.
Collaborate with cross-functional teams to align data engineering solutions with enterprise data strategy, including enforcing SLOs, monitoring standards, and data quality KPIs.
12 to 15 years of experience in Computer Science, IT or related field.
Hands-on experience with Databricks, PySpark, SparkSQL, Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies.
Strong understanding of AWS services and experience with workflow orchestration and big data performance tuning.
Preferred certifications: AWS Certified Data Engineer, Databricks Certificate, Scaled Agile SAFe certification.
Senior-level expert with deep experience in big data processing, distributed computing, and enterprise data architectures such as Data Fabric or Data Mesh.
Experienced in performance optimization, workflow orchestration, and integrating new technologies in an enterprise R&D environment.
Able to lead technical excellence, mentor engineers, and align cross-functional data engineering efforts to strategic enterprise goals.