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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Platform Engineer - **Company:** Aventum Group - **Location:** London, UK - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Data Analysis, Microsoft Azure, Continuous Integration, Data as a Services, Data Validation, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Distributed Computing Environment, Data Flow Control, Python (Programming Language), Metadata, NoSQL, Power BI, Backtesting, SQL Databases, Data Streaming, Tableau (Software), Management of Software Versions, Web Platforms, Apache Kafka, Data Pipelines, Serverless Computing, Databricks - **Published:** May 14, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=fab3402f1da16012 ## About the Role Do you have experience in Tableau?, * Strong experience with Python, SQL, and at least one NoSQL technology. * Hands-on depth in distributed data processing, ETL/ELT patterns, and data workflow orchestration (Airflow, dbt, ADF, Dagster). * Solid engineering practices around testing, CI/CD, monitoring, and deployment. * Experience with Kafka, EventHub, Kinesis, or similar distributed log technologies. * Strong understanding of streaming schemas, consumer group strategy, watermarking, and stateful processing. * Practical experience building or operating feature stores (Feast, Hopsworks, Databricks FS). * Understanding of feature definitions, point-in-time joins, online/offline store separation, and feature lifecycle governance. * Deep knowledge of canonical modelling, schema evolution, lineage systems, and metadata frameworks. * Expertise with data services in Azure, AWS, or GCP (ADF, Glue, Dataflow, Databricks, serverless compute). * Exposure to BI/MI tools (Power BI, Tableau). ## Description Event-Driven Telemetry & Behavioural Signals * Architect the event-driven telemetry backbone capturing user interactions, system behaviours, and workflow signals across all digital platforms. * Define consistent event schemas, taxonomies, and ACORD-aligned behavioural models. High-Scale, High-Reliability Data Pipelines * Build and operate batch and real-time pipelines to ingest, transform, enrich, and validate data from all digital suite products. * Automate data quality checks, lineage capture, and SLA monitoring through modern data observability patterns. Enterprise Feature Store Ownership * Develop and maintain the enterprise Feature Store (Feast, Hopsworks, Databricks FS). * Ensure features are consistent between training and inference, fully governed, and discoverable across teams. * Manage feature versioning, contracts, embeddings, and historical point-in-time correctness. Data Modelling, Schemas & Standards * Implement canonical schemas aligned to industry standards (ACORD-style entities) and internal capability matrices. * Shape unified data model powering Scope search, pricing, underwriting, claims, finance, and analytics. Data Governance, Lineage & Metadata * Drive end-to-end data governance: lineage, metadata cataloguing, validation frameworks, and schema evolution. * Ensure all data is traceable, trusted, and production-grade. ML Enablement & Historical Backtesting * Partner with ML engineers and scientists to create ML-ready feature datasets and transformations. * Support historical replay, time-travel datasets, and backtesting infrastructure for all digital platforms. Cross-Platform Engineering Collaboration * Work closely with product teams across the digital platforms to ensure smooth telemetry, consistent data flows, and scalable real-time integration patterns. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Data Analytics with Microsoft Fabric: End-to-End Use Case with Data Agents](https://www.wearedevelopers.com/videos/1547-data-analytics-with-microsoft-fabric-end-to-end-use-case-with-data-agents) - [Tomorrow's cloud data platforms - fully managed database-as-a-service (DBaaS)](https://www.wearedevelopers.com/videos/254-tomorrow-s-cloud-data-platforms-fully-managed-database-as-a-service-dbaas) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)