Analytics Engineer

Confido•New York, NY

5d•$150,000 - $190,000

About The Position

Confido is the AI infrastructure powering CPG brands from deduction to production plan. We unify cash application, deductions, disputes, trade promotion management, forecasting, demand planning, and analytics in one integrated platform. The result: measurable time savings, smarter top- and bottom-line decisions, and the speed to scale. Confido is trusted by 200+ brands managing $20B+ in revenue, including OLIPOP, Simple Mills, Dr. Squatch, Tropicana, and more. We’ve achieved best-in-class growth and recently raised a $15M Series A led by Footwork Ventures and Y Combinator to accelerate our momentum. The Role At Confido, data powers everything from customer-facing insights to internal decision making. We are looking for an Analytics / Data Engineer to build the foundation that enables scalable reporting, trusted metrics, and seamless data access across the company.

Requirements

Experience building and operating modern data warehouses and analytics platforms.
Strong SQL skills and experience modeling large datasets.
Deep experience with distributed data processing, including designing performant ETL pipelines and optimizing large-scale data workloads.
Experience with data pipeline orchestration, ETL/ELT workflows, and data infrastructure.
Familiarity with modern analytics tooling such as Snowflake, dbt, or similar technologies.
Ability to balance immediate business needs with long-term platform investments.
Excellent communication skills and a track record of working cross-functionally with technical and non-technical stakeholders.

Responsibilities

Design, build, and maintain our centralized data warehouse and analytics infrastructure.
Develop scalable data models that power reporting, dashboards, operational workflows, and customer-facing data exports.
Serve as the steward of Confido's data landscape, creating visibility into data ownership, lineage, and dependencies while ensuring data is discoverable, trustworthy, and easy to consume across the organization.
Consolidate and standardize data models across multiple products and teams, creating a single source of truth for the business.
Establish data engineering best practices, including data modeling standards, testing, documentation, monitoring, and governance.
Partner closely with Engineering, Product, Operations, and Customer teams to define metrics and deliver actionable insights.
Improve data quality, reliability, and accessibility across the organization.
Enable self-service analytics by creating scalable datasets and semantic layers for downstream consumers.