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