Distributed Query Platform Engineering

This platform gives analytics users one SQL surface over many data systems. The engineering problem is not the query engine itself — it is keeping a large, hybrid, multi-system estate predictable: multiple production clusters, cloud and on-prem environments, different data systems, different client and consumer technologies, identity and access that survive organizational change, frequent deployments, and production reliability — all working together without surprises.

• Production Trino / Starburst estate — carrying interactive analytics and federated data access.
• AWS + on-prem hybrid — the same platform in two very different environments, which forces portable automation instead of environment-specific tooling.
• Weekly deployment cadence — pipeline-gated, reversible rollouts to a live production fleet.
• On-call incident response — production reliability and diagnosis at fleet scale.

I work across multiple layers of this platform — source code, integrations, automation, pipelines, and production systems — not just the query engine. This was a team effort at Comcast. Where I contributed:

• Platform software — Java, Python, SQL, and shell development across the Trino codebase: proxy / integration components and query-performance engineering, plus code-level query/platform behavior troubleshooting.
• Infrastructure automation — declarative, reviewable infrastructure-as-code across the estate.
• Release engineering — CI/CD pipelines and deployment / release work with validation gates and rollback paths.
• Production reliability — observability, incident response, and fleet-scale troubleshooting.
• Identity / access integration — authentication and authorization across the query path and the data layer.
• Client / data-system interoperability — making heterogeneous consumers work reliably against the platform.

Trino / Starburst is the federation layer of a wider data estate. The interoperability engineering — keeping every consumer and source functional as systems evolve — is where the platform actually lives:

• Databricks / Unity Catalog — hands-on troubleshooting and integration work: validating communication between Databricks and the distributed query platform, covering connectivity, authentication / access, and query compatibility. This is integration and troubleshooting work — I did not administer or own the Databricks estate.
• Heterogeneous consumers — Snowflake, Spark, Tableau, Python clients / connectors, and SSIS, each spanning authentication, connectivity, query behavior, and platform compatibility.
• Lakehouse & object storage — Iceberg permissions / access-control work and S3-related platform migration that keeps the estate portable and operational.

Query platform & software

• Java / Python / SQL / shell across the Trino codebase — proxy and integration components let heterogeneous clients reach the platform predictably; query-performance work keeps interactive analytics responsive; query / platform behavior problems surface at the code level and get fixed there.
• Client-side spooling — large result sets written as segments to object storage so clients download in parallel, offloading I/O from the coordinator.
• Pre-release query replay / validation — candidate builds are re-run against stored queries so regressions are caught before rollout, not in production.

Identity & access

• Snowflake on-behalf-of authentication through Trino — users reach data with their own identities instead of shared credentials.
• Privacera-based access control integrated with OAuth / identity, enforcing authorization at the data layer.
• Iceberg permissions / access-control work extending access management to lakehouse formats.

Delivery & infrastructure

• Terraform + Ansible keep infrastructure declarative, reviewable, and portable across AWS and on-prem.
• GitHub Actions CI/CD with Kubernetes / Helm for platform workloads — pipeline-gated change with a rollback path for every release.
• S3-related migration work that keeps the estate portable and operational.

Security

• Security remediation across the toolchain (Snyk, CrowdStrike) plus access-layer hardening — security treated as an engineering discipline, not a separate team’s job.

Release engineering and reliability are one loop: a distributed production platform that ships weekly has to move fast and stay stable. Changes ship on a weekly cadence using zero-downtime deployment practices, so users can keep querying the platform while releases progress. The loop:

• Weekly cadence — nothing reaches the fleet outside the pipeline.
• Pre-release query replay validates candidate builds against stored queries before rollout.
• Zero-downtime deployment — releases roll out with rollback safety, so users keep querying the platform while changes ship; nothing takes the platform down.
• Observability — fleet-scale Prometheus / Grafana dashboards and alerting that support fast diagnosis across clusters.
• Incident response — on-call production work: diagnosing and remediating across clusters; runbooks and automation that remove repeat failure classes instead of paging through them.

Mean time to recovery cut from roughly 3 hours to roughly 1 hour through better observability and automation.

A weekly, validated release cadence operating against a live production fleet — the platform keeps evolving without production going dark.

This is software engineering across a distributed production platform, where code, infrastructure, identity, data systems, delivery, and reliability have to work together — Java / Python source work, integrations, automation, pipelines, and production systems, not just running a query engine. Declarative state, safe frequent change, observability you can act on, and interoperability across a heterogeneous data ecosystem. The same discipline that keeps a hybrid query estate dependable is the discipline the rest of the portfolio applies to other systems.