Lead Data Engineer

BÆRSkin Tactical Supply Co., part of Div Brands, is a remote-first outdoor gear company building durable, dependable products with a lean technical team. The Lead Data Engineer will own the data platform end to end, making architecture, reliability, cost, and tooling decisions while remaining hands-on. The role also partners with commercial teams, establishes engineering standards, enables self-service analytics, and mentors another data engineer without formal management responsibility.

Responsibilities:

• Own the architecture of the data platform end to end: ingestion, storage, transformation, modelling, serving
• Run it as well as design it. Build and buy calls, cost, reliability, performance
• Set the technical bar for the data team and be the person the CTO stops being the bottleneck for
• Stay hands-on. Most of your week is building, not reviewing
• Connect the platform to the commercial reality, and make the rest of the business able to self-serve off it
• Ingestion through serving. You will make the architectural calls on what we build, what we buy, and what we deprecate, and you will be accountable for the outcome
• Pipeline tuning, cost management, data quality, observability, incident response. Reliable enough to ship decisions off is the bar, not reliable enough to pass a test
• You should know where money leaks in a data platform and be able to argue managed versus self-hosted with numbers rather than preference
• Marketing, finance, operations. Attribution, cohorts, LTV, conversion, returns, fulfilment margins. You will translate commercial questions into models and datasets, and say no to the ones that should not be built
• The other data engineer gets someone senior to pair with. You set the patterns: modelling standards, review, testing, how AI gets used. You do this by shipping alongside them, not by writing a policy document
• Fewer ad-hoc requests every quarter. Documented models, coherent semantics, dashboards people trust without asking us to check them
• Claude Code, Cursor, Copilot, whatever your tools are. AI-augmented delivery is how you work already, and you will set the patterns the rest of the team follows

Requirements:

• At least 7 years hands-on in data engineering, data architecture or analytics engineering, including time owning a data platform end to end
• Advanced Python and SQL. Window functions, CTEs, query plans, partitioning. You can optimise, not just write
• You have designed and run a production data platform, and made architectural decisions that stuck. Not just executed someone else's blueprint
• Strong modelling judgement: warehouse, lake, lakehouse, and knowing which the problem actually needs
• Production experience with orchestration (Airflow, Dagster, Prefect) and transformation (dbt or equivalent)
• A cloud analytical warehouse at scale. BigQuery preferred, Snowflake, Redshift or ClickHouse also fine
• FinOps instinct. You have owned a platform budget and cut it without breaking things
• AI-native workflow. Daily use, with specific before and after examples of how it changed the way you build
• Commercial acumen. You can connect a platform decision to revenue, cost or customer impact
• English B2/C1 minimum
• 4h overlap with EU working hours
• Consumer-facing, high-volume commercial domain: DTC, retail, marketing or martech, gaming, subscription commerce
• Databricks and distributed processing (Spark, Polars, Dask)
• Streaming and near-real-time (Pub/Sub, Kafka)
• Terraform, Docker, CI/CD for data workflows, platform reliability practice
• T-shaped or pi-shaped background. Depth in one or two areas, breadth across several, ideally across more than one industry. Founder experience is a plus
• Mentoring experience without formal management
• Portuguese