Data Engineer (All Levels, Analytics & Platform) - UK Wide

Job DescriptionWe work with a range of UK employers actively hiring across these roles.Job Description:UK-Based (On-Site, Hybrid or Remote)About the Role We’re looking for experienced Data Engineers at all levels-from Data Engineer through to Senior, Lead, Principal, Analytics Engineer and Data Platform Engineer positions-for upcoming roles across data pipelines, warehouses, lakehouses and the platforms that data teams depend on. These are hands-on engineering roles where you’ll build and operate the foundations that analytics, BI, data science and ML all stand on.You’ll work across the full data engineering lifecycle-from ingestion and modelling through to transformation, orchestration, quality, observability and platform operation. The role suits someone who pairs strong software engineering discipline with genuine interest in data modelling and a pragmatic view of when to build, when to buy and when to leave it alone.Key Responsibilities Design, build and operate production ELT / ETL pipelines that move data reliably at the volume and latency the business needs Model data in the warehouse or lakehouse-dimensional, Data Vault, One Big Table or domain-driven approaches, fit to the use case Build and maintain transformation layers in dbt (or equivalent), with appropriate testing, documentation and lineage Operate orchestration tooling (Airflow, Dagster, Prefect or equivalent) and the workflows around it Manage cloud data warehouses and lakehouses (Snowflake, BigQuery, Redshift, Synapse, Databricks) Implement data quality, testing, monitoring and observability across pipelines and models Build streaming pipelines where the use case warrants it (Kafka, Kinesis, Pub/Sub, Flink) Partner with analysts, scientists, BI developers and ML engineers to deliver the data products they need Contribute to data governance, cataloguing, access management and platform security (Analytics Engineer) Sit closer to the analyst side-transformation, modelling, semantic layer, metrics (Platform Engineer) Sit closer to the platform side-infra, tooling, developer experience for data practitioners (For Senior/Lead/Principal) Set data architecture, mentor engineers, lead platform strategy and larger initiativesWhat You’ll BringTechnical Expertise: Strong SQL-comfortable with complex transformations, performance tuning and warehouse-specific dialect Strong Python skills (or Scala / Java where relevant) for pipeline development and tooling Hands-on experience with dbt or equivalent transformation tooling Orchestration experience (Airflow, Dagster, Prefect or equivalent) Production experience with at least one major cloud warehouse / lakehouse (Snowflake, BigQuery, Redshift, Synapse, Databricks) Cloud platform experience (AWS, GCP, Azure) Solid data modelling foundations-dimensional modelling, slowly changing dimensions, Data Vault or domain-driven approaches CI/CD for data pipelines, version control discipline, infrastructure-as-code basics (Terraform) Streaming and big data exposure (Kafka, Kinesis, Spark, Flink) is a plus Data quality and observability tooling (Great Expectations, Monte Carlo, Soda, dbt tests) is a plusEngineering & Soft Skills: Strong software engineering discipline-pipelines are products, not scripts Data quality mindset-if it’s not tested, it’s broken Pragmatic about the build-vs-buy and complexity-vs-utility trade-offs Strong communication with analysts, scientists and BI partners-you treat them as your users Collaborative approach with platform, ML and product peers Comfortable with ambiguity and shaping unclear data requirements into clean engineering scope Curiosity about the business problems the data underneath is servingDomain Flexibility: Roles span SaaS, fintech, retail, consumer, healthtech, public sector, media and B2B platforms Background in any of these is welcomed; appetite to learn an adjacent stack valued just as muchExperience Level: Minimum 2+ years for Data Engineer / Analytics Engineer, 5+ for Senior, 8+ for Lead / Principal Background in data engineering, analytics engineering, data platform engineering or related software discipline Examples of pipelines, models or platforms you’ve owned end-to-end in productionWhat We Offer Opportunity to work on data platforms that genuinely underpin analytics, BI, science and ML at meaningful scale Exposure to modern data tooling, cloud warehouses, transformation frameworks and orchestration platforms Roles at the level you’re ready for-we’re hiring across the IC data engineering spectrum A collaborative environment where engineering rigour and modelling quality are both valued Clear scope to develop specialist depth (streaming, platform, analytics engineering, modelling) or stay broad Flexible working arrangements (on-site, hybrid or remote) and supportive team culture