Role: Staff Engineer Customer Engineering (Data & AI Security Platforms)

Role Summary

Trust3 AI builds software that large enterprises use to control who can reach which data — across Databricks, Snowflake, AWS Lake Formation, BigQuery, Trino and more. It runs in production inside some of the world's largest banks, insurers, retailers and healthcare companies.

We are creating Customer Engineering, and you will be the first engineer in it. You will own a defined piece of that platform end to end — the connectors and integrations our enterprise customers run, the tooling around them, and everything it takes to make them fast, reliable and easy to operate. Design, build, ship, run, fix, improve, with no handoffs. Two engineers will join you to deliver it; the accountability stays yours.

Real workloads and real telemetry mean you never have to guess what to improve. Your job is to take software that works today and make it excellent — quicker to deploy, harder to break, cheaper to run. We build with AI: coding assistants and agents are part of the daily toolchain here, and we expect them to show up in what you ship.

What You'll Own

- The connectors and integrations customers run — quality, performance, release validation, security posture. If something is wrong with them it is yours, and so is the decision about how to fix it.
- Delivery speed. Customer-driven fixes and features shipped in weeks rather than release cycles, and custom connectors and integrations built to customer timelines.
- The hard problems. Connector failures, policy-sync faults, performance collapse, upgrade breakage — deep debugging, root cause, permanent fix.
- Deployment and operations. Install, upgrade and rollback automation across self-managed, SaaS and air-gapped estates; telemetry, alerting and dashboards covering connector health, performance and CVE exposure.

- Proving it works, and works at scale. Functional and scalability testing of what you own is yours, not someone else's gate to pass — the suites, the load profiles that reflect real customer volumes, and the evidence that a release is safe to ship.
- Scale, efficiency and toil. JVM and connector profiling,



throughput and resource envelopes at enterprise identity and policy volumes — and engineering away the procedures we repeat across customers, through self-healing behaviour rather than better runbooks.
- The technical call, and the team. What gets fixed, engineered away, declined or raised to Platform Engineering is your call; that boundary is agreed at leadership level, not renegotiated ticket by ticket.

You will hire and technically lead a connector engineer and a DevOps engineer.

The Engineering Bar Working software is the starting point, not the finish line. We want an engineer whose services are:

- Deployable — installs, upgrades and rolls back cleanly, without a runbook only the author can follow
- Scalable — holds at enterprise volumes and degrades predictably rather than falling over
- Lightweight — a modest footprint and resource envelopes that match the work being done, not the worst case someone once hit
- Enterprise-ready — hardened images, current dependencies, a clean CVE posture, and the evidence a customer's security team will ask for
- Provably correct — behaviour pinned down by tests you wrote, verified at customer-scale volumes before a customer finds the limit
- Supportable — diagnosable from logs and metrics by someone who did not write it
- Self-healing — any manual procedure we run twice is a defect: the second time it should be automated, the third time it should not be possible

Platform reliability is the customer's experience of us. The measure of this role is not how rapid you perform an operational task, but how quickly you make it unnecessary.

Skills Required

Real depth in the core, and the credibility to pick up the rest. Nobody has all of this.

- Core: Java and Spring at depth; microservices design and the reality of operating them; Kubernetes and Helm in production; Prometheus and Grafana; JVM debugging — heap and thread analysis, GC behaviour, connection handling under load; Linux; Python or Bash
- Data platforms,



depth in at least one: Databricks (Unity Catalog or SQL warehouses), Snowflake, AWS Lake Formation, BigQuery, Trino/Presto, Spark, or comparable
- Cloud, production depth in at least one: AWS, Azure or GCP — IAM, networking, managed Kubernetes
- Testing: functional, integration and load testing you build and own; test automation in CI. Maturity in spec-driven or behaviour-driven development is a strong plus — we would rather the expected behaviour be written down and executable than live in someone's head.
- Operations: CI/CD, TLS and certificate handling, container image hardening, dependency and CVE remediation; AI-assisted engineering as part of how you work
- Access control, familiarity is enough: RBAC, ABAC or tag-based policy, row and column level access, masking, enterprise SSO. We will teach you the governance domain; we cannot teach you ten years of production engineering.

Experience

- 8+ years building and operating production software, including time on systems you did not write
- Deep ownership of production failures in enterprise environments, with direct customer contact throughout
- Built Java microservices that other people deployed, scaled and supported without you in the room
- Raised the engineering standard of a live, revenue-carrying product — you can say what you changed, in what order, and how you did it without stopping delivery
- Comfortable as the senior technical voice in front of a customer's platform and security teams

Why This Role

- Genuine end-to-end ownership — one accountable owner, one piece of software, design through production, as a founding mandate
- Staff-level and hands-on — you lead technically, sit on the engineering ladder, and write code every week
- You define the function — what it takes on, what it refuses, and who joins it
- Problems with teeth, and impact you can see — enterprise-scale identity and policy volumes, JVM behaviour under load, multi-cloud deployment, security hardening against demanding review; what you ship reaches production customers in weeks

Location Pune, India • hybrid Reports to Field CTO & Head of Customer Experience • engineering ladder, calibrated with Engineering

📌 Staff Engineer -Customer Engineering (Data and AI Security Platforms) (Pune)
🏢 Privacera
📍 Pune