Data Engineer

You operate at the frontier of modern data engineering. You understand that AI is not a futureconsideration — it is a present-day design constraint. You build data infrastructure that is AI-ready by default: pipelines that serve feature stores, architectures that can support RAG andLLM applications, and platforms capable of integrating AI-assisted tooling at every stage of theengineering lifecycle. In a global team spanning Europe, the US, and India, you are a connector — bridging technicaldepth with business context, and aligning local delivery with global standards

• Technical Architecture & Delivery + Lead the end-to-end design and delivery of complex data engineering solutions on GCP —from architecture through production deployment + Architect scalable, cost-effective data platforms using BigQuery, Dataflow, Cloud Composer, Pub/Sub, Dataplex, and Cloud Storage + Design robust data models using Dimensional (Kimball), 3NF, and Data Vault methodologies— selecting the right approach for each use case + Implement SCD strategies and historical data management patterns for long-lived datasets + Lead the migration of legacy data structures to GCP, defining parallel testing and data parity validation strategies + Provision and govern cloud infrastructure using Terraform; champion IaC as a non-negotiable standard + Design and implement CI/CD pipelines for all data solutions — with automated testing, linting,and deployment gates
• AI-Era Responsibilities + AI-ready architecture: Design every data platform component to be downstream-AI-compatible — appropriate partitioning, feature store integration, and schema design for ML consumption + GenAI data infrastructure: Architect data pipelines for LLM-based applications, includingembedding generation pipelines, vector store population, and RAG data retrieval layers + Feature store engineering: Build and maintain centralised feature stores on Vertex AI, ensuring reproducibility and low-latency serving for ML models + AI-assisted development leadership: Champion GitHub Copilot, Gemini Code Assist, and Cursor as engineering productivity tools — set standards for how the team uses them responsibly + AI-powered data quality: Design ML-based anomaly detection into pipeline monitoring —moving beyond threshold alerts to intelligent pattern recognition + LLM Ops data layer: Build the data infrastructure that underpins model evaluation, fine-tuning dataset curation, and prompt tracking pipelines
• Leadership & Collaboration + Lead code reviews; hold the bar for quality, testability, and maintainability + Define and document reusable engineering patterns — pipeline templates, transformation standards, naming conventions + Actively mentor junior engineers through pairing, structured feedback, and technical design sessions + Work closely with global Data Engineering counterparts to align on platform standards + Engage directly with senior business stakeholders to translate complex requirements into technical solutions + Contribute to hiring: review take-home tasks, conduct technical interviews, calibrate assessments + Define and execute testing strategies for regulated workloads, including parallel-run validation against legacy systems
• Operational Excellence + Own pipeline reliability: define SLAs, implement alerting, lead incident resolution + Drive DataOps practices: automated testing, data contracts, observability-first design + Monitor and optimise GCP costs; propose and implement efficiency improvements + Ensure compliance with data security, encryption, and governance standards in all solutions built
• Essential — Technical + 5+ years of data engineering experience in production, cloud-native environments + 5+ years of advanced SQL: BigQuery specifics, query profiling, partitioning/clustering optimisation, complex analytical queries + 3+ years of GCP production experience: architecture design and delivery at scale + Deep, hands-on expertise across: BigQuery, Dataflow, Cloud Composer (Airflow), Pub/Sub, Dataplex, Cloud Storage, Terraform, Cloud Build + Mastery of data modelling methodologies: Dimensional/Kimball, 3NF, Data Vault — with real-world application of each + Production-level Python: OOP design patterns, async processing, unit/integration testing, GCP SDK usage + Demonstrated experience designing CI/CD pipelines for data products + Track record of leading legacy-to-cloud migrations
• Essential — Leadership & Professional + Demonstrated technical leadership: you have designed solutions, led reviews, and raised the quality bar of a team + Proven ability to work in high-ambiguity environments and drive clarity through technical design + Strong communication: able to write architecture decision records, run design reviews, and present to non-technical stakeholders + Evidence of mentoring junior engineers and improving team capability
• Desired + GCP Professional Data Engineer certification + Experience designing AI/ML data pipelines — feature stores, training data pipelines, Vertex AI integration + Hands-on experience with vector databases or embedding pipeline design + Active use of AI-assisted development tools (Copilot, Gemini, Cursor) in production delivery + Experience with dbt Core / Dataform in a production, team setting + Data engineering experience in a regulated financial environment (banking, insurance, credit) Experience designing event-driven architectures with Pub/Sub and Dataflow