Data Engineers, Analytics Engineers & Data Platform Experts.
We are seeking experienced UK-based Data Engineers, Analytics Engineers, Data Platform Engineers, and Data Architects for an intensive project supporting advanced AI research.
What You’ll Do
- Produce realistic data engineering work products based on production workflows
- Design and document ETL and ELT pipelines
- Build data models, transformation logic, and warehouse structures
- Create pipeline architecture documents, implementation plans, and technical specifications
- Work with batch and streaming data workflows
- Develop or review orchestration patterns, scheduling logic, and dependency management
- Produce SQL and Python-based data engineering solutions
- Document data quality checks, lineage, assumptions, and operational considerations
- Create examples that reflect how experienced data engineers make architectural and implementation decisions
- Collaborate with experts from adjacent domains on cross-functional tasks
- Contribute professional judgment that helps define evaluation criteria for advanced AI systems
Who Can Apply
Relevant backgrounds include:
Data Engineers, Senior Data Engineers, Staff Data Engineers, Principal Data Engineers, Lead Data Engineers, Data Engineering Consultants, and Data Engineering Specialists.
We also welcome:
Analytics Engineers, Senior Analytics Engineers, Data Platform Engineers, Data Infrastructure Engineers, Data Systems Engineers, Data Architects, Data Solution Architects, Cloud Data Engineers, and Data Warehouse Engineers.
Pipeline and integration backgrounds may include:
ETL Developers, ETL Engineers, ELT Engineers, Data Integration Engineers, Data Pipeline Engineers, Data Migration Engineers, Data Ingestion Engineers, Data Transformation Engineers, and Data Processing Engineers.
Platform and infrastructure backgrounds may include:
Big Data Engineers, Distributed Data Engineers, Streaming Data Engineers, Data Reliability Engineers, DataOps Engineers, Data Platform Architects, Cloud Data Architects, Data Warehouse Architects, and Data Infrastructure Specialists.
Relevant Data Engineering Experience
Strong candidates may have experience with:
- ETL pipelines
- ELT workflows
- Batch processing
- Real-time and streaming pipelines
- Data ingestion
- Data transformation
- Data cleansing
- Data validation
- Data quality monitoring
- Data lineage
- Schema design
- Data modeling
- Dimensional modeling
- Star and snowflake schemas
- Data warehouses
- Data lakes
- Lakehouse architectures
- Medallion architectures
- Data marts
- CDC pipelines
- Event-driven data systems
- Pipeline orchestration
- Workflow scheduling
- Dependency management
- Backfills and replay
- Idempotent pipeline design
- Data observability
- Pipeline performance optimization
- Cost optimization
- Data governance
- Production incident debugging
Programming & Query Languages
Strong candidates should have hands-on experience with:
SQL and Python
Data Engineering Tools
Experience with one or more of the following is highly relevant:
Apache Spark, PySpark, Kafka, Apache Flink, Apache Beam, Airflow, dbt, Dagster, Prefect, Luigi, Fivetran, Airbyte, Stitch, Matillion, Informatica, Talend, AWS Glue, Azure Data Factory, Google Cloud Dataflow, or similar data integration and orchestration tools.
Warehouses & Data Platforms
Relevant platform experience may include:
Snowflake, Google BigQuery, Databricks, Amazon Redshift, Microsoft Fabric, Azure Synapse Analytics, PostgreSQL, MySQL, SQL Server, Oracle, Teradata, ClickHouse, Trino, Presto, Hive, Delta Lake, Apache Iceberg, and Apache Hudi.
Cloud Experience
Experience across one or more major cloud platforms is valuable:
AWS, Google Cloud Platform, or Microsoft Azure.
Requirements
- Professional hands-on experience as a data engineer or closely related role
- Strong SQL and Python skills
- Experience building and owning production data pipelines
- Hands-on experience with ETL and/or ELT workflows
- Experience with orchestration, data warehousing, and modern data platforms
- Familiarity with batch and/or streaming architectures
- Experience working with cloud-based data infrastructure
- Strong understanding of production reliability, data quality, and maintainability
- Ability to create professional technical documentation and work products
- Strong written communication and technical judgment
- Ability to work independently during a short, intensive engagement
Preferred Background
- Senior, Staff, Principal, or Lead-level data engineering experience
- Experience owning large-scale production data platforms
- Strong experience with Spark, Kafka, Airflow, dbt, Snowflake, BigQuery, or Databricks
- Experience designing data architecture across AWS, GCP, or Azure
- Experience with both batch and real-time systems
- Experience reviewing or mentoring other data engineers
- Experience defining data engineering standards or architecture patterns
- Experience creating design documents, runbooks, data models, or technical specifications
This opportunity is ideal for hands-on UK data engineers who understand how production data systems are actually built, operated, debugged, scaled, and documented, and who can translate that experience into high-quality professional deliverables. We are a referral partner of the client.