We're hiring on behalf of our client, a leading global consumer insights and trend forecasting company that is expanding its AI & Data capability and building a new, greenfield data foundation. This is a hands-on, senior individual-contributor contract role for an engineer who wants real ownership of architecture decisions — not maintenance of someone else's stack.
You'll design, build, and optimise the data pipelines, models, and infrastructure that power classification systems, AI workflows, forecasting models, and consumer intelligence products. You'll work closely with senior data scientists, analysts, and engineers in cross-functional pods, and mentor a nearshore data engineering squad.
This is a fully remote contract, open to candidates across Europe, North America
What you'll do
* Design and build scalable, greenfield data architectures across Snowflake and cloud environments using Infrastructure-as-Code.
* Lead schema design — primarily on a Data Vault 2.0 backbone (hubs, links, satellites) with a dbt mesh pattern, though broader data modelling experience is welcome — to support analytics and AI workloads.
* Own complex SQL development and performance tuning — optimising costly queries, improving warehouse efficiency, and setting best-practice standards.
* Build robust, idempotent ETL/ELT pipelines: brokers land data to S3 (Parquet/Iceberg), with dbt/Airflow loaders into the Data Vault, including rate limiting, retries, and schema-drift handling.
* Implement pipelines using Python and dbt (PySpark a plus), across Snowflake and distributed compute environments.
* Apply Snowflake cost governance, RBAC, Managed Access Schemas, and OIDC Workload Identity Federation across a Dev/QA/Prod account topology.
* Build data quality and observability: Great Expectations, dbt tests, data contracts, lineage, Grafana dashboards, and the Airflow UI.
* Own the data-platform Terraform repo and CI/CD workflows (GitHub Actions), with quality gates via SQLFluff and dbt-checkpoint.
* Build and maintain orchestration on Managed Workflows for Apache Airflow (MWAA / Airflow 3.x), using Cosmos for granular, per-model task observability.
* Mentor nearshore engineers and contribute to team-wide engineering maturity.
What we're looking for
* 5+ years hands-on as a Data Engineer.
* Proven success designing and scaling production-grade, greenfield platforms using Data Vault 2.0.
* Expert-level SQL: complex queries, optimisation, performance tuning, analytical SQL.
* Advanced data modelling — Data Vault 2.0 as the primary approach, with dimensional or other modelling experience valued.
* Strong Python: Pandas, NumPy, and dbt (PySpark/Snowpark a plus).
* Deep Snowflake experience (multi-account topology, RBAC, Managed Access Schemas, PrivateLink, governance).
* Airflow orchestration (MWAA / Airflow 3.x) and dbt orchestration with Cosmos.
* Infrastructure-as-Code with Terraform; dedicated Terraform repo management.
* CI/CD for data pipelines (GitHub Actions, CircleCI); data quality tooling (SQLFluff, dbt-checkpoint).
* Strong AWS or Azure knowledge, including dedicated DE AWS accounts and hub-and-spoke networking.
* Experience building resilient, idempotent API ingestion pipelines landing to S3 (Parquet/Iceberg).
* Working knowledge of Docker, Linux, and cloud VMs; OIDC Workload Identity Federation for credentials.
* Experience mentoring engineers, ideally in a nearshore/offshore model.
* Nice to have: Databricks and distributed compute; domain-by-domain migration using a strangler-fig pattern with parallel running and regression testing.
Why this one
* Greenfield build — you shape the architecture from the ground up.
* A modern, opinionated stack: Snowflake, dbt, Airflow, Terraform, Data Vault 2.0.
* High-impact data powering AI and consumer intelligence products at global scale.
Interested? Apply here or reach out directly.