Sr. Manager, Data & Analytics

Senior Manager, Data & Analytics Engineering

Lead data platform teams and drive decision‑making across the company.

• Define and own the multi‑year roadmap for the data platform, aligning investments in infrastructure, tooling, and headcount with business strategy.
• Lead and grow the Data and Analytics team, cultivating a collaborative, feedback‑rich environment with clear career pathways.
• Architect and oversee scalable data pipelines across ingestion, transformation, orchestration, and delivery for both batch and streaming use cases.
• Champion best practices in analytics engineering, including semantic layer design, dbt modeling standards, data contracts, and metrics governance.
• Partner with business stakeholders to deliver high‑quality, self‑serve data solutions aligned to business needs.
• Ensure data platform reliability, observability, SLAs, and incident response, treating the platform as a product with real users.
• Drive vendor and tool evaluations for the modern data stack (cloud warehouse, orchestration, cataloging, transformation, reverse ETL, etc.).
• Set and enforce data quality, documentation, and governance standards to build trust across the business.
• Champion use of AI coding assistants and LLM‑powered tooling (e.g., Cursor, Git Hub Copilot, Claude) to accelerate delivery and reduce toil.
• Implement AI‑native patterns such as LLM‑generated documentation, anomaly detection, data quality monitoring, and automated root‑cause analysis.
• Prototype natural‑language interfaces (NL‑to‑SQL and AI‑powered BI tools) to empower self‑serve analytics for non‑technical users.
• Build foundational data infrastructure (feature stores, vector stores, model metadata, evaluation datasets) to enable AI and ML experimentation and scale.

• 7+ years in data engineering or analytics engineering, with at least 3 years in a senior leadership role managing multiple teams.
• Deep expertise in the modern data stack—cloud data warehouses (Snowflake, Big Query, or Databricks), dbt, orchestration tools (Airflow, Dagster, or Prefect), and ELT frameworks.
• Strong command of SQL and Python.
• Hands‑on experience integrating AI/LLM tooling into engineering workflows or data products.
• Proven ability to define and execute a multi‑year data platform strategy.
• Strong stakeholder management, including executive presentations and translating technical concepts to non‑technical audiences.
• Experience building and scaling high‑performing engineering teams: hiring, mentoring, performance management.
• Track record of delivering trusted, well‑documented, and widely adopted data products.
• Preferred:
  Familiarity with semantic layer tools (e.g., Metric Flow, Cube), data cataloging (e.g., Atlan, Datahub), and data observability platforms.
• Preferred:
  Experience with streaming data (Kafka, Flink, or Kinesis) and batch processing.
• Preferred:
  Exposure to data mesh or data product organizational models.

We are committed to building a diverse and inclusive workforce where all people thrive.