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Strong employer brand, mid-level generalist title, metro location, and broad in-demand skillset increase competition.
Core data engineering skills are transferable across industries, though pharma data governance introduces some domain bias.
Explicit 5–8 year requirement plus mandatory Databricks, Spark, Python, SQL, and ETL skills increase filter strictness.
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Design, build, and maintain cloud-based applications, APIs (REST, SOAP, Bulk), and large data pipelines using Databricks and ETL tools.
Develop, optimize, and support data workflows and reporting to enable actionable business insights and decisions across geographic regions.
Collaborate with Data Architects and SMEs, adhere to coding/testing best practices, and participate in CI/CD DevOps processes for software quality and release management.
Bachelor’s or Master’s degree in Computer Science, Data Science, IT or related field.
5 to 8 years of relevant work experience in software engineering with big data technologies.
Experience with Python, PySpark, SQL, Databricks, REST APIs, cloud platforms (AWS, Azure, or GCP), and ETL tools such as Informatica.
Not explicitly mentioned: No explicit notice period or mandatory location mentioned.
Experienced in building and optimizing large-scale data pipelines and applying data governance and compliance (e.g., GDPR, CCPA).
Proficient with cloud data architectures including AWS Lambda, S3, EMR, EC2, and data modeling expertise with PostgreSQL and Salesforce object models.
Comfortable working in cross-regional teams with exposure to CI/CD pipelines, automated testing, and mentoring junior developers.