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Strong employer and metro location but senior, niche principal data engineering reduces applicant density.
Core data engineering skills are transferable, though pharma domain knowledge is preferred.
Explicit 12–17 years plus mandatory Databricks/Spark/AWS skills make filters highly strict.
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Own design, development, and optimization of complex data pipelines and metadata-driven data engineering frameworks using Databricks, Spark, and Delta Lake.
Lead adoption of emerging big data technologies, drive performance tuning (Spark, job scheduling, query improvements), and establish data quality KPIs and monitoring for production pipelines.
Collaborate cross-functionally to align data engineering strategies with enterprise goals, mentor engineers, and support scalable, governed data products within a data fabric or data mesh architecture.
12 to 17 years of work experience in Computer Science, IT, or related field.
Hands-on expertise with Databricks, PySpark, SparkSQL, Apache Spark, AWS services, Python, SQL, and workflow orchestration.
Experience with Scaled Agile Framework (SAFe), Agile delivery, and DevOps practices.
Relevant certifications preferred but not mandatory: AWS Certified Data Engineer, Databricks Certificate, Scaled Agile SAFe certification.
Deep experience in designing and tuning scalable big data pipelines with hands-on use of Spark and Databricks in production environments.
Familiarity with enterprise data architectures like Data Fabric or Data Mesh and integrating them across governance, analytics, and platform teams.
Proven ability to lead technical excellence, mentor teams, and work closely with cross-functional global teams in an Agile and DevOps environment.