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.

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.

We are looking for a Senior Data Engineer to:

• 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.

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.

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

How You Work

• 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

• 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

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.

• 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

• 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

Apply here with a CV. A cover letter is optional.

Can I apply for both the Senior and the Lead Data Engineer role

No. Pick one. Applications to both roles will be disqualified from both. If you are unsure which fits apply for the one you want and say so in your application. We move candidates between the two ourselves when the interview says we should.

How long is the contract

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.

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.