Senior Director, Artificial Intelligence - Cambridge - Insight Global

    Insight Global
    Insight Global Cambridge

    1 day ago

    Pharmaceutical / Bio-tech
    Description

    Position: Sr Director, AI

    Location: Hybrid 3x/week in Cambridge, MA

    Duration: Permanent / Direct Hire

    Pay range: $210-230k base

    Job Description:

    Insight Global is seeking a Senior Director to join a leading biotech company and shape how data and information solutions enable the delivery of life‑changing medicines to patients. This is a hands‑on technical leadership role, responsible for architecting solutions, evaluating vendor platforms, occasionally writing code, and leading a high‑performing engineering team. This leader will own the data engineering capability across the R&D and manufacturing value chain.

    This role serves as the primary technology interface for Data, AI, and Engineering across Research, Early Development, and Technical Operations & Quality. You will act as both a strategic and hands‑on bridge between the enterprise data platform and the technical business functions that rely on it, ensuring data, AI‑enabled solutions, and engineering standards are deeply embedded into how these functions operate and innovate.

    You will lead a team of 10 data engineers and data management professionals and be directly accountable for all engineering delivery in this space. You will be responsible for growing the team by hiring roughly 8 more individuals. You will build and operate data infrastructure, pipelines, and governance capabilities on or adjacent to the Snowflake Lakehouse, enabling manufacturing analytics, research data integration, quality intelligence, and supply chain planning, in close partnership with IT Vertical Leadership for Research & Early Development (ReDev) and Technical Operations & Quality (TOQ).

    Responsibilities:

    • Own the data platform and systems engineering capability for ReDev & TOQ, delivering reliable, scalable, and GxP‑compliant data services.
    • Architect and modernize end‑to‑end data integrations across lab, manufacturing, quality, and enterprise systems using a modern data stack. (Fivetran, dbt, Snowflake, Astronomer, semantic layers).
    • Lead the transition from legacy batch ETL and point‑to‑point integrations to automated ingestion (e.g., Fivetran), continuous loading (Snowpipe), and governed Lakehouse architectures.
    • Serve as the accountable technical authority—setting architectural standards, reviewing designs and vendor proposals, and guiding complex integrations across MES, ELN, LIMS, SAP, and QMS.
    • Ensure data pipelines, models, and semantic layers (Python, SQL) support analytics, regulatory submissions, batch release, and advanced use cases including AI/ML.
    • Implement CI/CD and infrastructure‑as‑code for data platforms using GitHub Actions, Terraform, and dbt testing, with strong observability and data quality controls.
    • Establish strong data governance, lineage, master data, and validation practices to meet regulatory and quality requirements.
    • Build, mentor, and lead a high‑performing data engineering team, owning delivery, quality, and continuous improvement.
    • Partner closely with IT and business leaders as a true "two‑in‑a‑box" owner of outcomes.
    • Champion a product‑oriented, value‑driven operating model—and love great engineering.

    Must Haves:

    • 10+ years building production data platforms and pipelines.
    • 5+ years technical leadership experience.
    • Experience with ELT/ETL, lakehouse architectures, streaming, cloud‑native platforms.
    • Expert Snowflake experience: data modeling, performance tuning, Snowpipe, data sharing.
    • Strong Databricks experience for analytics and ML workloads
    • Hands‑on with Fivetran, Airflow/Astronomer, dbt, and data observability tools (e.g., Monte Carlo).
    • Expert SQL and Python; production‑quality code standards and reviews
    • Manufacturing and enterprise system integration experience, including SAP (PP, MM, QM) and MES.
    • Enterprise data architecture ownership: technical specs, integration patterns, vendor due diligence.
    • Data system validation and risk‑based CSV experience in regulated environments.
    • Compliant data systems: audit trails, access controls, e‑signatures, data integrity, validated pipelines.
    • Delivery of complex, concurrent initiatives in regulated, data‑intensive environments.
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