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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Engineer - **Company:** BRISCO CONTRACTING LLC - **Location:** Dover, DE, United States (Remote available) - **Experience:** Expert - **Contract:** Temporary to permanent - **Skills:** Artificial Intelligence, Airflow, BigQuery, Continuous Integration, Data Architecture, Information Engineering, Data Infrastructure, Cursor (Graphical User Interface Elements), Distributed Computing Environment, Github, Python (Programming Language), Operational Databases, Performance Tuning, Software Architecture, SQL Databases, Data Streaming, Google Data Studio, System Availability, Snowflake, Apache Spark, Dask, Apache Kafka, Vertica, Terraform, Docker, Databricks - **Published:** August 13, 2026 - **Apply:** https://www.careerjet.com/job/us8f42d19150cc1ea4c106dbfd5b28b01b/eaa ## About the Role 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 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 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 ## Description 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. What Will You Do? 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. Our Stack 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., * 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 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 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. 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