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# Senior Data Engineer - Life Sciences.AI

- **Company:** McKinsey & Company
- **Location:** Boston, MA, United States
- **Experience:** Expert
- **Salary:** $176,000 - $179,500
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Amazon Web Services, Microsoft Azure, BigQuery, Continuous Integration, Data Cleansing, Information Engineering, Python (Programming Language), PostgreSQL, Machine Learning, Search Technologies, Software Engineering, SQL Databases, Systems Integration, Web Application Frameworks, Data Processing, Google Cloud, Data Storage Technologies, LangChain, Snowflake, Apache Spark, Generative AI, Langfuse — LLM Observability and Analytics Platform, Agentic-AI, Cloudformation, Pandas, Pyspark, LangSmith, Information Technology, Low Latency, Data Management, Machine Learning Operations, Terraform, Data Pipelines, Databricks
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/7_0DA39DC900DB22305E87E36662517755

## About the Role

There is flexibility to hire at the Senior Data Engineer I/II or Principal Data Engineer I/II level, depending on your experience.Your backgroundDegree in computer science, business analytics, engineering, mathematics, or a related field5+ years of professional experience in data engineering, software engineering, or adjacent technical roles Proficiency in Python for production-grade pipelines, with strong skills in SQL and PySpark Proven experience building end-to-end data pipelines and platforms for Agentic AI, Generative AI, Machine Learning, or Business Intelligence, covering data preparation, embeddings generation, vector search, and system integration using modern frameworks (Pandas, Spark, dbt, LangChain, LangGraph).
Familiarity with workflow orchestration tools such as Temporal, CI/CD for data workflows, and infrastructure-as-code (Terraform, CloudFormation) Familiarity with vector databases and understanding of low latency serving patterns is a plus Experience building systems with different data formats (structured vs unstructured) and data processing methods (streaming vs batch) and deploying across major cloud platforms (AWS, Azure, GCP) A strong foundation in system design, data storage, and reliability with commonly used data platforms (Databricks, Databricks Asset Bundles, Snowflake, BigQuery, PSQL, etc.) Understanding of MLOps and LLMOps principles including pipeline monitoring, testing, and evals, with exposure to observability tools such as Langfuse, LangSmith, and Opik Strong communication skills, both verbal and written, in English, with the ability to adjust your style to suit different perspectives and seniority levels