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Confido is an AI infrastructure platform serving 200+ CPG brands (including OLIPOP, Simple Mills, Dr. Squatch, Tropicana) to manage finance, accounting, sales, and operations in a unified system. The company is growing 5x year-over-year with a small team in NYC.
You will lead Confido's data function, which currently spans multiple systems and ownership structures. Your mission is to centralize, standardize, and scale the data infrastructure that powers customer-facing reports, internal dashboards, and company-wide decision-making.
You'll start by managing the Senior Analytics Engineer and will hire additional data engineers as the team grows. Early on, you'll contribute significantly to hands-on work—writing SQL, building models, and reviewing code. Over time, your focus will shift toward hiring, planning, and cross-functional collaboration.
Key responsibilities include:
- Managing and developing the data team, with hiring authority as the team expands
- Owning technical decisions on warehouse design, pipelines, orchestration, and tooling
- Defining and enforcing standards for data modeling, testing, documentation, monitoring, and on-call practices
- Collaborating with Engineering, Product, Ops, and Customer teams to align on metric definitions and data requirements
- Taking accountability for data quality and pipeline reliability, implementing systemic fixes
- Writing and reviewing code, particularly in the early phase
- Communicating prioritization decisions clearly to the broader organization
Success looks like: within one year, the company trusts the data, knows where to find it, and uses a unified set of core models. Pipelines are more reliable, issues are caught proactively, and the team has grown with clear onboarding standards.
REQUIREMENTS:
- 7+ years in data or analytics engineering, with at least 2 years managing engineers
- Proven experience building a data warehouse from an early or messy state and maintaining it in production
- Strong SQL and data modeling skills; hands-on experience building and maintaining ETL/ELT pipelines in production
- Experience with Snowflake, dbt, Airflow, Dagster, or similar tools
- Track record hiring engineers and developing their capabilities
- Ability to translate vague requirements (e.g., "we need better reporting") into concrete plans
- Comfort splitting time between management and hands-on technical work
