Senior Data Engineer

You build and run the pipelines, models and warehouse the commercial side makes decisions off. Python, SQL, dbt, Airflow, BigQuery. Four years plus, hands-on, AI already in your daily workflow. You will be the third person on a two-person data team, with no layers between you and the decisions. Fully remote, EU or LATAM, minimum 4h EU overlap., * Build and run the pipelines, models and warehouse that the commercial side makes decisions off

• Own reliability and cost of what you build, not just the happy path
• Work hands-on with BigQuery, dbt, Airflow and dlt
• Use AI in your daily workflow, not as a side experiment
• Join a two-person data team that is about to grow, with real ownership from week one

This is an individual contributor role. Nobody reports to you and nobody will scope your work for you. What Will You Do? Build and run the platform Ingestion, transformation, modelling and serving. Fivetran and custom extracts in, dbt in the middle, BigQuery and Metabase out, Airflow holding it together. You build it, you run it. Model data the business can actually use Attribution, cohorts, LTV, conversion, returns, fulfilment. You will work directly with marketing, finance and operations, translate what they are asking for into datasets that answer the question, and push back when the question is wrong. Own reliability Tests, monitoring, alerting, freshness, and fixing your own breakages. If someone finds a broken number before you do, that is a miss. Watch the money Warehouse and tooling cost is part of the job. You should know what your queries and pipelines cost and care about it. Make the business self-serve The goal is fewer ad-hoc requests over time, not more. Documented models, sensible semantics, dashboards people trust. Ship with AI Claude Code, Cursor, Copilot, whatever your tools are. They are part of how you work daily and they make you meaningfully faster. Our Stack Python, SQL, BigQuery, dbt-core, Airflow (on GKE), Fivetran, GCS, Databricks, Metabase, Looker Studio, Docker, Terraform, GitHub Actions, Linear, Notion. You do not need to know all of it. Smart people learn the stack fast.

We expect at least 3 years hands-on in data engineering or a similar role. You do not need to tick every box. If you are curious, adaptable and quick to learn, we want to hear from you. Essential

• Advanced Python and SQL. You can write, read and optimise real queries, not just get them to return rows
• dbt or equivalent transformation tooling in production
• Orchestration in production: Airflow, Dagster or Prefect
• Solid data modelling: warehouses, lakes, lakehouses, and knowing which one the problem needs
• A cloud analytical warehouse. BigQuery preferred, Snowflake, Redshift or ClickHouse also fine
• AI-native workflow. You use AI coding tools daily and can walk us through a specific recent example: what you asked, what came back, what you had to fix
• English B2/C1 minimum
• 4h overlap with EU working hours

Desirable

• Consumer-facing, high-volume commercial domain: DTC, retail, marketing or martech, gaming, subscription commerce. You know why attribution, cohorting and LTV matter
• Databricks or distributed processing (Spark, Polars, Dask)
• Streaming and near-real-time (Pub/Sub, Kafka)
• Docker, Terraform, CI/CD for data workflows
• Portuguese

• Hands-on. You build things yourself. You can point to what you personally shipped in the last 90 days
• Pragmatic. You know when good enough is good enough, and you can defend the corner you cut
• Self-directed. “Here is the problem, figure it out” sounds good to you
• Commercially aware. You understand the platform exists to serve the business
• Direct. We say what we mean. No politics, no passive aggression
• Coachable. You take feedback and something actually changes

How We Work

• Remote-first, async-first. We measure output, not hours
• Hyper-horizontal. Titles exist for external context. Internally the best argument wins
• Loose on structure. We run light process. People own what they build
• Fast. We ship, learn, iterate. Blameless post-mortems when things break, and things break
• Forward deployed, as a direction. The long-term goal is for engineers, data included, to own areas of the business rather than run a service desk. We are not fully there yet. This role helps get us there

What Success Looks Like At 90 days: the pipelines you own are more reliable than when you arrived, at least one team outside data is making decisions off something you built, and your AI-augmented workflow is visible in how fast you move. This Role Won’t Suit You If

• You need a PM, a ticket and three meetings before you can start
• You are a pure analyst. This role builds the platform, it does not only query off it
• Your experience is mostly large enterprise with big teams and narrow scope
• AI is something you talk about but have not shipped with
• Heavy process and ceremonies are core to how you work

What’s In It For You?

• Fully remote
• Competitive salary with regular performance reviews
• 26 days paid leave
• Parental leave
• Training and budget towards professional-level cloud certification
• Genuinely strong colleagues, and no layers between you and the decisions

• A stack you can change. If something is wrong, you fix it, Full-time permanent with a 6-month trial period. We are looking for long-term team members, not short-term contractors. What is the interview process? Four stages. Stage 1 is a 30-minute initial interview. Stage 2 is a 60-minute technical conversation on architecture, cost and business context. Stage 3 is 60 minutes hands-on with SQL and Python, AI use expected. Stage 4 is a 90-minute panel with three of us, with a short break in the middle. No whiteboard puzzles, no trick questions. What is your AI policy in this process? We are heavy AI users, but we do not bolt an LLM onto everything.

• You can and should use any LLM during the technical stages, and on the job
• We do not use AI to screen applications. A human reads every one
• A well-crafted cover letter is good. No cover letter is fine. A lazy LLM-generated one is worse than nothing

How you use AI weighs more than whether you use it, and we do expect you to use it. Will I get feedback if I am not selected? Yes, if you get past the initial screen and ask for it. For early-stage applications we cannot give individual feedback. Do you work with recruiters? No. We run our own hiring. Please do not contact us with recruiting services. Equal Opportunity BÆRSkin Tactical Supply Co. / Div Brands is an equal opportunity employer. We welcome applications from candidates of all backgrounds and evaluate people on skills, qualifications and ability to do the job. If you need any accommodation during the application or interview process, tell us. BÆRSkin Tactical Supply Co. + Div Brands

BÆRSkin Tactical Supply Co. combines rugged performance with everyday functionality to make the very best outdoor gear. We design apparel that reflects timeless values, resilience and practicality, so anyone can stay prepared and unstoppable, on the trail, in the city or wherever life takes them. More than a brand, BÆRSkin is a mindset. Our remote-first team spans borders but shares a single focus: building durable, dependable products. Our motto is “Built for the Brave”. We are small and we punch well above our weight technically. The tech org is eight people: six on software, two on data. Engineering has had zero turnover in four years. We have been shipping AI-written code to production since 2023.