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Mid-level generalist data engineer role in metro with strong brand and broad skillset increases competition.
Core data engineering skills are highly transferable; life‑sciences experience is advantageous but optional.
Explicit 4–5+ years requirement and mandatory stack (Spark, Snowflake, Python) raises shortlisting strictness.
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Design, develop, and maintain scalable data engineering solutions and pipelines to support AI, ML, and advanced analytics initiatives.
Collaborate with business and product teams to understand requirements and translate them into technical designs meeting data quality, security, and governance standards.
Contribute to data engineering best practices, frameworks, and community, ensuring code quality, automated testing, and CI/CD integration.
Bachelor's degree or equivalent in Computer Science, Engineering, or relevant field.
4 to 5+ years of experience in data engineering, integration, data warehousing, business intelligence, or related roles with technologies like Spark/Scala, Informatica/IICS/Dbt.
Proficient in SQL, scripting languages (Python, Shell), and cloud-based data platforms such as Snowflake.
Experience with job scheduling/orchestration tools (Airflow is a plus); knowledge of data structures, algorithms, and query tuning.
Experienced handling complex data architecture challenges within cross-functional teams in fast-paced environments.
Technically proficient with a pragmatic approach to problem-solving and strong attention to detail.
Comfortable working in agile/scrum environments and effectively communicating technical solutions to both peers and leadership.