Principal Data Engineer

Monthly salary: $7,500 - $9,000 USD

Along with our partner, we are seeking a Principal Data Engineer who will own the technical direction of our partner's data platform: the architecture, standards, and decisions behind the data their own teams rely on, the data shared across their products, and the data products they sell.

You will lead a team of senior and mid level data engineers who are smart and have strong opinions of their own. Your job is to hear them out, make the call, explain it well enough that the team can carry it forward, and hold the line when the evidence is on your side. When the evidence is not, you change your mind and say so.

This is a hands on role. You will write code, review designs, and step into the hardest production issues. But your main output is judgment: which problems matter, which patterns become standard, which shortcuts you will not take, and which customer requests become product.

Our partner builds AI products, and they expect their engineers to use AI tools daily:
Claude, Claude Code, and Copilot for code, review, documentation, and debugging. At this level they also expect you to set the team's position on where AI belongs in the data platform and where deterministic, verifiable approaches are required. Much of their data is finance grade, and accuracy is not negotiable.

• Set and own the technical direction of the data platform: architecture, modeling standards, ingestion patterns, and a 12 to 24 month technical roadmap of platform investments.
• Make the consequential technical decisions and write them down: the tradeoffs, the options rejected, and what would make you revisit the decision.
• Resolve technical disagreements on the team. Hear every argument, decide, and own the outcome.
• Challenge Product, Engineering, and leadership with evidence when you think a direction is wrong. Once a decision is made, commit to it fully.
• Own the Snowflake architecture end to end: medallion layers, the Data Vault 2.0 core, CDC from product databases, secure data sharing to customer accounts, performance, and cost.
• Model the cost of architecture choices before they are made, and keep Snowflake and tooling spend tied to value.
• Define the team's engineering standards: naming conventions for every Snowflake object, testing and data quality gates in the pull request, CI/CD, and release practice, aligned with our partner's company engineering standards.
• Decide which internal and customer implementation patterns become reusable platform capabilities, and which stay one off.
• Run design reviews and set the code review bar. Raise the quality of how senior engineers frame and defend their own proposals.
• Act as the technical authority with customer data teams and partners: explain our partner's architecture, data guarantees, and tradeoffs plainly.
• Develop the team: mentor engineers to senior and senior engineers toward principal level judgment, give direct technical feedback, and help hire.
• Write production code and lead the response on the hardest incidents.
• Lead technical roadmap refinement sessions
• Own the technical implementation of master data management across the portfolio: one governed, trusted version of core entities such as properties, ownership and management companies, and accounts, shared by every product, internal system, and customer deliverable.
• Shape the internal data products the business runs on (analytics, operational reporting, and AI foundations) with the same rigor as customer facing products.

• 10+ years in data engineering, including 3+ years as a technical lead, staff, principal, or architect who owned platform direction for a team.
• Deep production experience with Snowflake: architecture, performance tuning, cost management, and secure data sharing.
• Expert SQL and dbt, including project structure, testing strategy, and CI/CD for data.
• Deep data modeling across dimensional (Kimball) and Data Vault 2.0 or comparable normalized patterns, and a clear view of when each fits.
• Designed semantic layers or governed metrics so BI and AI consumers get one consistent, traceable definition of each measure.
• Designed and operated production ingestion at scale: CDC, orchestration, monitoring, and recovery.
• A track record of consequential technical decisions that held up, with written reasoning (design docs, decision records). You can also walk them through one you got wrong and what you…