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Popular data-engineer title, broad skills, and metro hiring increase competition, tempered by senior 7+ years requirement.
Core data engineering skills are transferable, though pharmaceutical domain knowledge increases specificity.
Explicit 7+ years plus mandatory cloud, big-data, and pipeline expertise makes shortlisting highly strict.
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Design and implement scalable data collection, storage, and processing pipelines to support enterprise-wide data needs, enabling advanced analytics and self-service reporting.
Maintain data governance frameworks and quality checks to ensure compliance, reliability, and secure data infrastructure.
Provide technical leadership in data platform architecture, collaborate with stakeholders to prioritize data initiatives, and operationalize data science models into production pipelines.
Bachelor's degree in Engineering, Analytics/Data Science, Computer Science, Statistics or equivalent experience.
7+ years of experience with big data technologies such as Python, PySpark, SQL and AWS data services (Redshift, S3, Glue, Lambda, Postgres).
Experience building batch, micro-batch, and streaming pipelines; strong knowledge of data modeling techniques (Data Vault 2.0, Dimensional Modeling, Knowledge Graphs).
Experience with workflow orchestration tools like Airflow; familiarity with data governance frameworks and data quality tools (e.g., Great Expectations).
Experienced in designing enterprise-scale data platforms integrating data lakehouse, warehouse, and marts with scalable pipelines.
Able to collaborate cross-functionally translating business needs into scalable data solutions with a strategic mindset on data roadmap and cost optimization.
Knowledge of AI/ML concepts, generative AI patterns, and exposure to pharmaceutical or life sciences data is an advantage.