Lead Engineer, Data Platform
at Tide
India, Delhi NCR, senior
posted 02 Nov 2026
- Java
- BigQuery
- Infrastructure as Code
- Coralogix
- GitHub
- Claude
- Spring Boot
- Looker
- Atlan
- Okta
- SQL
- Apache Airflow
- Terraform
- dbt
- CI/CD
- ConductorOne
- Databricks
- Data Modeling
- Python
- Fivetran
About the role
The Data Platform Engineering team builds the platform that Tide's data producers and consumers rely on: backend engineers, data engineers, data scientists and, increasingly, AI agents. We own how data is produced, moved, stored, modelled, governed and served across the UK, EU and India, on GCP with BigQuery at the core.
Our mission is to make the right way the easy way: self-service capabilities with quality, security and cost controls built into the infrastructure, judged by whether teams choose to adopt them, not whether we mandate them.
As Lead Engineer, Data Platform, you will be a hands-on individual contributor who leads platform initiatives within a clear technical direction set by the Data Infrastructure team. You will own small-medium platform initiatives and quarterly goals, taking them from design through to shipping and running them in production, and you will work closely with the Data Infrastructure Engineering Manager and the wider platform team.
What you would do
- Design and deliver self-service paths so engineers can onboard new sources, datasets and pipelines without a ticket queue, with access, masking, quality and cost controls defined in infrastructure as code and CI/CD.
- Improve ingestion and transformation capabilities so data is fresher and more reliable, handling ordering, duplicates, late data, deletes, replay and backfill, and migrate pipelines without breaking downstream tables.
- Apply governance in what you build: residency, PII masking, least-privilege and just-in-time access, retention and erasure, and lineage.
- Set SLOs, freshness and quality checks, and alerting that tells on-call what matters.
- Find the main cost drivers in data platforms and pipelines, and add guardrails and attribution that teams can act on.
- Break work into phases that ship, with clear milestones, a migration path and a way to measure adoption.
- Use AI-assisted development to build and run the platform faster, and help prepare governed, well-described data that agents can use safely.
- Treat internal engineers as customers: write clear documentation and golden paths, and act on their feedback.
- Review designs and code for other engineers, and raise the bar for production-grade data and API work.
Read from job-boards.greenhouse.io, last seen 4d ago.