Lead Data Engineer

Staff-level scope lead title. Read the first section carefully: this is not a management role.

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 intend to stay lean. We are not building big teams we are raising the ceiling on what a small one can do.

We are looking for a Lead Data Engineer to take our data function from two engineers running hard to a platform that scales with the business.

• 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

This is not a management hire. Nobody reports to you. You lead through building through architecture calls and through raising the standard of what the other engineer ships. If you have been a senior engineer told you are too senior to just code read on.

Own the platform at a system level

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. Fivetran Airflow on GKE BigQuery dbt-core Databricks GCS Metabase. Some of it is right. Some of it is not. Part of the job is telling us which.

Run it day to day

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.

Treat cost as a first-class concern

You should know where money leaks in a data platform and be able to argue managed versus self-hosted with numbers rather than preference.

Partner with the commercial side

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.

Raise the bar on the team

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.

Build the self-serve layer

Fewer ad-hoc requests every quarter. Documented models coherent semantics dashboards people trust without asking us to check them.

Ship with AI

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.

Python SQL BigQuery dbt-core Airflow (on GKE) Fivetran GCS Databricks Metabase Looker Studio Docker Terraform GitHub Actions Linear Notion.

Familiarity helps but is not required. Smart people learn the stack fast.

We expect at least 7 years hands-on in data engineering data architecture or analytics engineering including time owning a data platform end to end.

Essential

• 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 elses 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

Desirable

• 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

How You Work

• Hands-on. You build. Delegators who cannot do the work themselves are a no
• Pragmatic. You make hard calls and compromises to keep flow and you can defend the corner you cut
• Self-directed. Here is the problem figure it out is the whole job
• Direct. We say what we mean. No politics no passive aggression
• Team player. No brilliant assholes. We do not hire them and we do not keep them
• Coachable. You are senior not finished

• 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 cycle through and own areas of the business so tech becomes an augmentation of every other function rather than a service desk. We are not fully there. This hire is critical to building the framework that gets us there

At 90 days: the platform is more reliable and more self-serviceable than when you arrived the business is making decisions off data you own the other data engineer has someone senior to learn from and the CTO is no longer the bottleneck on data architecture decisions.

• You want to build a team first and execute later. We need execution from week one
• You need a PM a ticket and three meetings before you can start
• You are a pure analyst or a pure architect. This role builds
• 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
• You want a management track. There is no team to manage here and we are not planning one

• Fully remote
• Competitive salary with regular performance reviews
• 26 days paid leave
• Parental leave
• Training and budget towards professional-level cloud certification
• Architectural authority from day one with a CTO who wants to hand it over
• A stack you can change. If something is wrong you fix it

Apply here with a CV. A cover letter is optional. If you write one tell us about a platform you owned and what you would do differently.

Can I apply for both the Lead and the Senior 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.

Is this a management role

No. No direct reports. You lead through architecture standards and shipping. If you want people management this is not it.

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 inter...