Data Platform Lead

This is a fully remote position, open to applicants in United States.

📋 Description

• Take charge of the data platform strategy from end to end — encompassing ingestion architecture, scalable multi-tenant data infrastructure, transformation pipelines, a modern BI layer, and the platform's growth as we onboard new clients and data sources.

• Spearhead the AI-on-data layer. Collaborate with our AI engineer to determine how agents and LLMs can access, query, and manipulate platform data — focusing on semantic models, retrieval patterns, and in-warehouse AI primitives.

• Develop and productize our integration processes — efficiently ingesting data from an expanding range of first- and third-party sources. Transform what currently necessitates custom work into a standardized, repeatable process.

• Lead and nurture our data engineering team. You will manage directly, establish technical direction, and elevate the quality of work — starting with a cohesive team that has room for expansion.

• Set the engineering standards for the data organization — including CI/CD, testing, infrastructure as code, data lineage, governance, and observability — to ensure the platform scales robustly.

• Maintain a strong hands-on presence in the warehouse and transformation layer — be sufficiently fluent in Snowflake to actively contribute alongside the team, rather than merely supervising.

• Collaborate with go-to-market teams to establish what excellence looks like for client onboarding and data delivery, driving engineering execution toward that standard.

⛳️ Requirements

• 8+ years of experience in data engineering, including a minimum of 2 years in a leadership capacity.

• Strong instinct for people management: effective communicator, skilled in talent development, comfortable providing direct feedback, and able to cultivate a high-performance culture within a small team.

• Practical experience with a contemporary cloud data warehouse and transformation stack — Snowflake + dbt is highly preferred; experience with Redshift, Databricks, or BigQuery with a quick learning curve is acceptable.

• Demonstrated experience in building AI solutions on structured data — including semantic layers, agent/LLM access patterns to warehouses, or retrieval-augmented generation.

• Extensive expertise in large-scale data ingestion — you have developed or managed systems that aggregate data from numerous disparate sources into a warehouse. You understand when to utilize an off-the-shelf connector, when to build one, and how to ensure both are operable at scale.

• Experience in developing and delivering a productized, multi-tenant data offering — encompassing client isolation, onboarding processes, SLAs, and ongoing support. You think in terms of products rather than projects.

• Strong engineering fundamentals: version control, code review, CI/CD, infrastructure as code — with a preference for standards that teams can replicate, avoiding reliance on individual heroics.

🏝️ Benefits

• Remote-first work environment

• Competitive salary and equity options

• Flexible PTO policy

• 401(k) plan

• Comprehensive medical, dental, and vision insurance plans

• 16 weeks of paid parental leave for both primary and secondary caregivers

• $1,000 reimbursement for home office technology setup

• Company-sponsored sustainability subscription to maintain carbon neutrality for employee activities, including travel