As a Senior Data Engineer, you are a technical leader within the Data Engineering organisation. You don't just build — you architect, mentor, and set the standard. You lead high-priority initiatives end to end, from requirements through to production, and your decisions meaningfully shape the team's technical direction.

You operate at the frontier of up-to-date data engineering. You understand that AI is not a future consideration — 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 and LLM applications, and platforms capable of integrating AI-assisted tooling at every stage of the engineering lifecycle.

In a global team spanning Europe, the US, and India, you are a connector — bridging technical depth with business context, and aligning local delivery with global standards.

What You Will Do

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, including embedding 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- LLMOps 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

What We're Looking For

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- Minimum 2:2 degree (or international equivalent) in Computer Science or related technical field; demonstrated professional experience considered in lieu for internal applicants

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

📌 Senior Data Engineer (GCP) (Mumbai)
🏢 Quantiphi
📍 Mumbai