A senior second opinion on the stack you already run. I audit production data pipelines — design, cost, CI/CD, orchestration, governance — find what's costing you money or about to break, and either fix it or hand your team a prioritized plan. No team to staff, no project to pad.
I audit the modern AWS data stack end to end — ingestion through to the warehouse — going deep on a deliberately narrow set of tools rather than dabbling in everything. Each finding comes with a cost, a risk, or an incident waiting to happen attached.
My focus for the last three years has been the modern data stack — deliberately deep on a defined set of tools rather than dabbling in everything. Here is where I actually work:
The modern data stack is my primary environment — Fivetran → Snowflake → dbt → Airflow is where I spend most of my time. Beyond the pipeline itself, I have designed custom data quality frameworks with spike detection, wired Airflow failure alerts, and set up PagerDuty on-call rotations so teams know about problems before their stakeholders do. AWS is the infrastructure layer underneath most of this work.
Short and scoped by design. The audit stands on its own — you get a clear picture whether or not you bring me back to fix anything. Two doors out of step three.
An audit is a handful of focused hours, not a sprawling retainer. We agree the scope and a cap before I start, so you get the flexibility of hourly with the predictability of a fixed engagement. Start with the audit — take the fix work further only if it is worth it.
Based in Bengaluru, India — overlapping hours with US clients agreed upfront.
If the question is "is this design right, what will it cost, and how long will it take" rather than "what is broken in what we run" — start with DataFoundry. It is built for data engineers validating a design, data teams estimating pipeline cost, and product owners who know the requirement but not the implementation, timeline, or spend. Answer a few questions, get a recommended stack. Free — and a fair sample of how I think about architecture.
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I am Supreeth Gowda, a data engineer with 12+ years across building and testing data infrastructure — seven years in California as a test/software and big data engineer and senior SDET at Sonos and Meredith, then a data engineer at Amazon in Seattle working a 45–50 petabyte data warehouse. That mix matters: I have built these pipelines and spent years systematically breaking them to find what fails. I now run this practice independently from Bangalore.
I started Encore because the most expensive data problems are not the ones in greenfield design decks — they are the ones already running in production, quietly costing money and breaking at 3am. Development tells me what good looks like; testing tells me how to find what is broken; design review tells me what the right fix is. That combination is the audit: I go into a stack already in production, find what is wrong, and either fix it or show the team how.
I keep this deliberately solo and scoped. You get a senior engineer hands-on with your problem — not a sales call followed by a junior doing the actual work. When you need more hands than I have, I will tell you honestly.
◆ Recently completed the UC Berkeley Professional Certificate in Machine Learning & Artificial Intelligence — extending the same rigor into ML/AI data infrastructure.
Book a 30-minute scoping call. No pitch — just an honest read on whether an audit is worth your time and what it would cover.
Book a stack audit call →
