Tech Lead Data Engineer

Our Data Analytics & BI team

We are a small, senior, fully remote team running analytics for the entire company on Snowflake, dbt, Matillion, and Sigma. We move fast and ship things people use daily. What we're missing is a senior technical voice to make the foundational calls — architecture, platform, governance, ML Ops — so that speed compounds instead of accumulating debt. That's this role, and it comes with unusual latitude to decide how the platform should work.

How You'll Make an Impact

As our Tech Lead Data Engineer & Data Architect, you will shape the technical direction, architectural standards, and data governance of our core Data Analytics platform. This is a hands-on leadership role: you will act as our Data Architect—owning logical and physical data modeling, warehouse design, and layered curation (staging → intermediate → mart)—while serving as our Data Engineering Tech Lead.

You will help continuously modernize our tech stack, elevating our software engineering discipline across our data pipelines (DataOps/CI-CD), optimizing compute performance and costs, driving MLOps workflows, and reinforcing data quality and security standards. Crucially, you will drive our AI-readiness initiatives—architecting the semantic layers, data structures, and access patterns needed to make our data seamless and reliable for AI agents and automated workflows. You will serve as the technical anchor for a talented team of Analytical Engineers, collaborating closely with Engineering, Infrastructure, Data Science / ML and business stakeholders to ensure our data ecosystem is reliable, scalable, and business-ready.

What You Bring to the Team

• Data Architecture & Modeling Ownership: Set and evolve our cloud data architecture on Snowflake. Own data model design across data products and marts, implementing flexible dimensional models (Star Schemas, SCDs, conformed dimensions) and documenting decisions through Architecture Decision Records (ADRs).
• Data Engineering Competency: Lead the end-to-end design and execution of data pipelines—owning ingestion, API integrations, custom connectors, ELT/ETL processes, and pipeline orchestration.
• DataOps & CI/CD Integration: Build upon our existing Git, CI/CD, and Jira workflows by embedding specialized data discipline—such as automated dbt testing on pull requests, environment isolation, automated credential management, and data deployment standards.
• Governance & Observability Strategy: Build upon and refine the initial foundations of our governance model. Evaluate, select, and implement scalable Data Catalog and Data Observability solutions to track data lineage, ensure freshness, monitor completeness, and automate failure alerting.
• Platform Efficiency & Cost Control: Continuously tune Snowflake query performance, clustering, and warehouse sizing to optimize execution times and compute spend.
• Technical Mentorship & Agile Leadership: Raise the technical effectiveness of Analytical Engineers within our Agile planning framework through architectural guidance, design reviews, and constructive challenge.
• Stack Modernization & Architecture Evolution: Continuously evaluate new tools, frameworks, and architecture patterns to modernize, optimize, and add flexibility to our data stack.

What Sets You Up for Success

• Senior Hands-On Experience: Proven track record as a Senior Data Engineer, Data Architect, or Tech Lead in cloud data environments.
• AI & Agent Readiness: Exposure to preparing data structures, semantic models, feature stores, and API/access layers for AI agents, machine learning pipelines, and automated LLM workflows.
• Snowflake & dbt Mastery: Deep hands-on expertise administering, tuning, and modeling in Snowflake, alongside advanced mastery of dbt (macros, incremental models, custom tests, packages, and dbt-native CI/CD testing).
• Advanced SQL & Python Skills: Expert analytical SQL skills (query execution plan tuning, window functions, CTEs) and strong Python proficiency for data pipelines, scripting, and MLOps workflows.
• Orchestration & Tool Evaluation: Practical experience with ingestion and orchestration engines (e.g., Matillion, Apache Airflow, Prefect, Dagster, Fivetran), with a strong ability to evaluate architectural trade-offs, performance, and costs when selecting or migrating platforms.
• DataOps & Software Engineering Discipline: Experience operating with active Git and CI/CD pipelines, with a track record of extending these systems to include automated data testing, deployment validation, and secure credential rotation.
• Critical Thinking & Problem Decomposition: Tendency to question assumptions, gather full business context, form hypotheses, and rigorously evaluate outputs (especially for AI/ML outputs) before jumping to technical solutions.
• Pragmatic Experimentation: Passion for building lightweight MVPs and Proofs of Concept (POCs) to evaluate new technologies, test architectural hypotheses, and modernize the data stack.
• Communication & Technical Influence: Business-level English fluency with the ability to articulate complex technical trade-offs clearly, mentor teammates, and collaborate effectively in a remote, globally distributed team.

Bonus Skills to Stand Out

• Exposure to applied AI tooling (Snowflake Cortex, AI Classify, RAG workflows, LLM agents, MCP servers).
• Familiarity with modern BI consumption tools like Sigma or workflow engines like n8n.

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