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Strong legacy brand, metro location, and a generalist senior data engineer profile with broad required skills.
Core data engineering skills transfer across industries, though healthcare domain and compliance add moderate specialization.
Explicit multi-year experience bands plus mandatory cloud, Databricks/Snowflake, and data-quality requirements indicate high strictness.
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Design, build, and maintain complex scalable ETL/ELT data pipelines using cloud platforms such as Snowflake or Databricks.
Support AI/GenAI applications by embedding generation and vector-based pipelines, including offline model training and low-latency inference workloads.
Lead data quality, validation, monitoring, and semantic data modeling efforts to provide trustworthy analytics-ready datasets while collaborating with cross-functional business and technical teams.
Bachelor’s degree in Computer Science, Mathematics, Engineering or related technical field with 6-8 years related experience OR High School Diploma/GED with 10 years related experience.
Strong experience with SQL, relational and NoSQL databases.
Proven hands-on experience with large-scale data pipelines in cloud environments (Azure or AWS) using Databricks or Snowflake and distributed processing frameworks like Spark.
Proficiency in Python, data-frame libraries (e.g., Pandas, Polars), and ETL/workflow orchestration tools (e.g., Databricks Workflows, Azure Data Factory, AWS Glue).
Experienced data engineer comfortable designing and operating complex enterprise-scale data architectures with a focus on scalability, performance, and maintainability.
Skilled in cross-domain collaboration across healthcare, manufacturing, analytics, AI/ML, and business teams to translate data needs into technical solutions.
Able to provide technical leadership and mentorship promoting best practices, data quality, and continuous improvement in a regulated healthcare environment.