Director, Data Engineering

New

SQUIREBusiness management software

Remote - US or CanadaFull-TimeDirector

SalaryBase ($200,000 - $230,000) + Bonus (15%)

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Job Details

Experience: 8+ years in data or software engineering, including 4+ years managing engineers
Required Skills: PythonSQLApache AirflowSnowflakeData engineeringdbt

Have 8+ years of experience in data or software engineering, including 4+ years managing engineers.
Have experience building and leading a multidisciplinary data organization of 5+ people across data, ML, analytics, or software.
Bring advanced SQL and Python skills and strong MDM fundamentals.
Have hands-on experience with Snowflake or an equivalent data warehouse.
Have hands-on experience with dbt and an orchestration tool such as Airflow, Dagster, or Prefect.
Have direct experience setting AI and LLM enablement strategy and building with LLM tooling.
Be able to own budgets, advocate for roadmaps, and translate technical tradeoffs for non-technical stakeholders while remaining involved in technical execution.
Experience building AI agents with LLMs, including tool use, retrieval, and orchestration, is a nice-to-have.
Backend engineering experience, ideally in JavaScript or TypeScript, is a nice-to-have.
Strong ML and MLOps knowledge is a nice-to-have.
Experience with Salesforce, Stripe, Gong, or Polytomic reverse ETL, and company-wide data governance or PII handling, is a nice-to-have.

Design, build, and debug high-impact pipelines connecting business systems, including CRM, billing, and internal platforms, to the Snowflake warehouse in both directions.
Own data strategy and architecture across a multi-year horizon while maintaining hands-on familiarity with systems and their edge cases.
Establish standards for pipeline design, data modeling, and data quality, and personally build and ship high-risk or ambiguous work.
Build and lead a growing Data organization of 5+ across Data Engineering, ML Engineering, and Analytics.
Own headcount planning, hiring strategy, and budget for the Data organization.
Partner with the Chief Product and Technology Officer and cross-functional leaders to translate company strategy into data contracts and platform investments.
Audit existing integrations and automations and determine what to replace and how to prioritize that work.
Enable AI and automation company-wide by ensuring agents and LLM use cases are built on a trusted data foundation, and build select enablement capabilities hands-on.

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