Principal Data Engineer

Tecla; United States; Remote; USD 84,000 - 100,000; Full time

Benefits offered by this job

Remote work

Paid vacation

Leadership ownership

Job summary

Tecla is seeking a Principal Data Engineer to own the technical direction of the partner's data platform, shaping architecture, standards, and long‑term roadmaps. You will lead senior engineers, write production code, and resolve complex production issues with a data-first mindset.

You will steer Snowflake architecture, Data Vault 2.0 patterns, CDC, secure data sharing, and cost governance, while balancing performance, reliability, and business impact.

Qualifications

• 10+ years in data engineering with 3+ years in a technical lead/architect role
• Deep production experience with Snowflake: architecture, performance tuning, cost management, secure data sharing
• Expert SQL and dbt, including project structure, testing strategy, and CI/CD for data
• Deep data modeling across Kimball dimensional modeling and Data Vault 2.0
• Designed semantic layers with governed metrics for consistent BI/AI definitions
• Designed and operated production data ingestion at scale (CDC, orchestration, monitoring, recovery)
• Track record of consequential technical decisions with written justification (design docs/records)
• Led engineers to decisions and commitments without relying on titles
• Pushed back on leadership with evidence and adjusted stance when needed
• Weighed cost, time, and customer impact alongside technical quality
• Clear writing and speaking from design docs to executive explanations
• Master data management practices: entity resolution, survivorship, governance for reference data
• Built and supported internal data products and customer-facing data products

Responsibilities

• Set and own the technical direction of the data platform (architecture, modeling standards, ingestion patterns, 12–24 month roadmap)
• Make consequential technical decisions and document tradeoffs and rejected options
• Resolve technical disagreements and drive outcomes
• Lead Snowflake architecture end-to-end including core Vault patterns, CDC, data sharing, performance, and cost
• Define engineering standards, naming conventions, testing gates, CI/CD, and release practices
• Decide reusable platform capabilities vs one-offs and guide implementation patterns
• Run design reviews and set code review bar for senior engineers
• Act as technical authority with customer data teams and partners: explain architecture and guarantees
• Develop the team: mentor engineers toward senior/principal levels and help hire
• Write production code and lead incident responses
• Lead technical roadmap refinement sessions
• Own master data management across portfolio: governed core entities
• Shape internal data products (analytics, reporting, AI foundations) with rigor

Skills

Snowflake

SQL

dbt

Data Vault 2.0

Kimball modeling

Data governance

Tools

CI/CD

Job description

Native/Bilingual English is required for this role (read/written/spoken)

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.

How They Work:

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.

What You'll Do:

• 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.

What They're Looking For:

Must Have:

• 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 changed.
• Led strong, opinionated engineers to a decision and to real commitment, without relying on title.
• Has pushed back on leadership with evidence and changed an outcome. Has also changed their own position when the evidence went the other way.
• Weighs cost, time, and customer impact alongside technical quality.
• Clear writing and speaking, from a design doc for engineers to a one page explanation for an executive or a customer.
• Master data management in practice: entity resolution, golden records, survivorship rules, and governance for reference data shared across multiple source systems.
• Built and supported both internal data products (company analytics, operational reporting, AI foundations) and customer facing data products.

Nice to Have:

• Built or ran customer facing data products: data sharing, managed warehouses, embedded analytics, or data marketplaces.
• Multi tenant data architecture, or consolidating data platforms after an acquisition or merger.
• Hospitality, finance, labor, or accounting data.
• Experience implementing Data Mesh
• Built data for LLM or conversational analytics where every answer must trace back to source.
• Python for tooling, validation, and automation.

What Success Looks Like:

• In your first 30 days, you will learn the platform, the product domains, and the team, and publish a written assessment of the top architecture risks and the decisions you would revisit.
• By 60 days, you will run design reviews and the decision record process, and will have made and documented your first significant architecture decisions.
• By 90 days, the team will work to standards you set, and you will have a 12 month platform roadmap agreed with D&A leadership and the CTO. Engineers bring technical disagreements to you, and your calls stick.

Why This Role Matters:

Our partner runs on its data, and increasingly sells it. At this stage every architecture decision compounds across every internal team that relies on the platform and every customer they onboard. This role makes those decisions, and makes them well.

Please note our partner is only looking for full-time dedicated team members who are eager to fully integrate within their team.

• A fully remote position with a structured schedule that supports work-life balance.
• Two weeks of paid vacation per year.
• Technical Leadership & Ownership:Lead the architecture, standards, and technical direction of the data platform.