Enterprise data platform consulting

Audit what you run.
Build what you don't.

Modern data platform consulting for growing companies. I uncover risks in the stack you already run—or design and build a production-ready platform on AWS and Snowflake from the ground up. Two focused services backed by 12+ years of enterprise engineering experience.

Book a call Choose your path ↓
12+
Years enterprise
engineering
~50%
Pipeline runtime
reduction
$150
Per hour
agreed cap
SERVICE 01 · AUDIT
If you have a stack

Find what's wrong. Fix what matters.

Costs keep rising. Pipelines fail unexpectedly. Stakeholders lose trust before your alerts fire. Or the one engineer who understands everything is about to leave.

  • Snowflake spend climbing with no clear cause
  • Silent pipeline failures and weak alerting
  • Schema drift and access changes without guardrails
SERVICE 02 · BUILD
If you need a stack

Build it right, the first time.

You have data sources and a clear goal, but no one on the team who's designed and shipped a production-grade data platform before.

  • Choose the right architecture from day one
  • Understand operational costs before you build
  • Ship production-ready instead of learning in production
01
Service One
Stack Audit

Find what's wrong.
Fix what matters.

I audit modern data platforms end to end—from ingestion to the warehouse. Rather than claiming expertise in everything, I go deep on the technologies I know best. Every finding ties back to cost, reliability, security, or operational risk.

You built it. You made the tool choices, hired the team, shipped it. For a while, it worked.

But somewhere in the last year, things quietly started going wrong.

The weekly report that used to land by Monday morning is now failing over the weekend — and by the time anyone notices, the stakeholder meeting has already started without reliable numbers. The monthly rollover jobs that ran in 2 hours are now taking 6, sometimes failing halfway with no clear reason. Your Snowflake bill went up 40% last quarter. Your AWS bill followed. The data volume didn't. The system is working harder to do less.

The team is shipping more than ever. Production deployments are up. But so are the bugs. Fixes that close one ticket open two more. Things that worked fine a year ago have quietly started breaking — not loudly, but slowly, in ways that only surface when a stakeholder notices a number that doesn't look right.

Your CI/CD is feeling it too. Getting a feature through dev, staging, and prod is starting to feel like a project in itself. The pipeline that was supposed to speed things up has become something the team works around.

The codebase that started clean is starting to look like the system it was supposed to replace. Patches on patches. Jobs nobody fully understands. The engineer who built the critical pipeline left eight months ago. The documentation was never written.

And now stakeholders are asking questions you can't confidently answer.

"Why did the revenue number change between Monday and Thursday?" "The report says X but the dashboard says Y — which one do we trust?" "Can we add this new data source? How long will it take?"

You already know something is wrong. You just don't know what, or how bad, or what it will take to fix it. And the longer you leave it, the worse it gets.

~/audit/symptoms — does any of this sound familiar?
✗We're migrating platforms and aren't sure the new architecture actually fitsmigration
✗Snowflake costs keep climbing and nobody knows whycost
✗When a pipeline fails we hear it from a stakeholder — not from an alertreliability
✗We can't easily trace or debug our pipelinesorchestration
✗One engineer holds all the platform knowledgekey-person risk
✗Snowflake access, roles, and schema changes have grown without guardrailsgovernance
✗Changes reach production without meaningful checksCI/CD
→Some problems are obvious. Others stay hidden until production finds them. A good audit catches both.
01 / cost

Spend climbing without answers

Oversized warehouses, poor auto-suspend settings, inefficient queries, and unnecessary compute all add up. I identify where the money is going and provide practical recommendations to reduce cost without sacrificing performance — this approach cut critical pipeline runtimes by ~50% in a recent engagement.

02 / design & migration

Validate before you commit budget

Every platform involves trade-offs. I pressure-test architecture decisions before they're expensive to change—from ingestion patterns to storage formats, orchestration, and warehouse design.

03 / reliability & orchestration

Failures you find out about late

Reliable pipelines are more than successful DAG runs. I review orchestration, monitoring, alerting, and operational processes so problems surface before your stakeholders find them.

04 / governance & CI/CD

Access and code without guardrails

Access controls, schema changes, deployment workflows, and CI/CD often evolve without discipline. I put practical guardrails in place so teams can move faster without increasing risk.

Typical modern data platform I audit
Fivetran
Fivetran
Ingestion
→
Snowflake
Snowflake
Warehouse
→
dbt
dbt
Transform
→
Airflow
Airflow
Orchestrate
→
Power BI
Power BI
BI

Every platform is different. I also audit AWS-native stacks (Redshift, EMR, Glue) just as often. The tools matter less than the patterns—ingestion reliability, transformation quality, orchestration discipline, and the observability that catches failures before your stakeholders do.

Explore the architecture showcase →
Real platforms — ingestion, warehouse, orchestration, observability
How the audit works

Hourly, bounded, no surprises.

A handful of focused hours, not a sprawling retainer. We agree the scope and a cap before I start. Take the report and implement it yourself, or bring me back to fix the highest-impact issues.

$150
/ hr
typical audit: 8–16 hrs · agreed cap
Book an audit call →
A free tool I built

Want a fast read on your stack's risks?

StackAudit gives you a structured risk report for your modern data stack in under a minute — cost traps, reliability gaps, scale limits, and the anti-patterns specific to your tools. An instant, automated first pass on the same questions my full audit digs into. Free, opinionated, and a fair sample of how I think about risk.

StackAudit Try StackAudit →
02
Service Two
End-to-End Build

Build it right,
the first time.

For growing companies building a modern data platform without an in-house architect. I turn your requirements—data sources, scale, compliance, budget, and team capability—into a production-ready architecture, then build it end to end. I make the important decisions before the first line of code is written.

You know what you need to build. You're less sure how to build it.

You've done the research. Each architecture looks reasonable until you map it onto your actual situation — your team, your budget, your data sources, your timeline. And then the questions stack up.

"Which ingestion approach is right for us? What's the most cost-efficient path? What's easiest to maintain when something breaks at 3am?" "What can my team actually own long-term? Do I need orchestration yet? How do I build observability so I know before a stakeholder does?" "How do we build a CI/CD flow that minimises errors, moves fast, and holds up under pressure — one that gives the team confidence to ship, not anxiety about what breaks next?"

The decisions you make now shape what you're living with for the next three years. The wrong call means rebuilding. The right call means you're building on your infrastructure, not fighting it.

The anxiety isn't about not knowing the tools. It's about not having a senior voice in the room who's made these calls before and can tell you what's actually worth worrying about.

~/build/symptoms — starting from zero looks like this
✗We need a production data platform, but nobody here has built one beforegreenfield
✗We don't know if we need Snowflake, Redshift, or something else entirelytool choice
✗Compliance can't be an afterthoughtcompliance
✗We don't know what this will actually cost to run at our real volumecost unknown
✗We need to ship—not spend months debating architecturetimeline
✗We want to own and operate it ourselves once it's builtownership
→Every one of these becomes more expensive after the first release. Good architecture prevents expensive rewrites later.
01 / architecture

Requirements, turned into a design

Every successful platform starts with good decisions. I design the integration approach, security model, deployment strategy, and operating model before development begins—reducing rework once implementation starts.

02 / scale

Built to survive the next order of magnitude

Platforms should scale predictably—not require redesign every time data volumes grow. I design for operational scale from day one, balancing performance, cost, and maintainability as workloads evolve.

03 / batch & streaming

Whatever shape your data arrives in

Batch, streaming, APIs, SaaS integrations, flat files—it doesn't matter. The ingestion approach should match the source and operational requirements, not force every workload through the same pattern.

04 / observability

Alerts before your stakeholders notice

Monitoring, alerting, and data quality aren't features you bolt on later. They are part of the platform from day one, so failures are detected before your customers or stakeholders notice them.

Platform technologies
AWS
S3 Lambda Redshift EMR Glue Athena MWAA Firehose Kinesis EventBridge DynamoDB Lake Formation IAM Secrets Manager
CI/CD & IaC
GitHub GitHub Actions Terraform Docker Jenkins

Some platforms rely on batch pipelines, others on streaming, event-driven architectures, or a combination of both. I choose technologies based on operational needs, team capability, and long-term maintainability—not because they're fashionable.

Explore the architecture showcase →
Real platforms — ingestion, warehouse, orchestration, observability
How the build works

Project-based. Fully focused.

Build engagements run over weeks or months, not hours. I take on a limited number at a time so each project gets full attention. Architecture decisions happen upfront, with phased delivery and clear milestones throughout.

Based in Bengaluru, India — overlapping hours with US clients agreed upfront.

$100
/ hr
typical build: 3–6 months · limited slots
Book a build call →
A free tool I built

Planning a platform before you build?

Not ready to hire yet? Start with DataFoundry. Answer a few questions about your requirements and get a recommended architecture, technology stack, cost estimate, and implementation plan—for free. It's the same architecture-first thinking I use in consulting, packaged as a self-service tool. When you're ready to build it in production, that is exactly what the build service above is for.

DataFoundry Try DataFoundry →
How it works

One process, either path.

Whether it's an audit or a build: scope and budget agreed before I start, findings or code delivered in writing, and everything you need to operate independently at handoff.

01
STEP 01

Scoping call

A focused 30-minute call to understand your platform, goals, and constraints. You'll leave with a clear read on what the engagement would involve and whether it's the right fit.

02
STEP 02

Audit — or architecture

Existing platform: I review the architecture, identify risks, and prioritize what matters most. Greenfield: we define the architecture, delivery phases, and technical decisions before implementation begins.

03
STEP 03

Deliver — or hand off

You can implement the recommendations yourself or have me deliver them. Either way, you own the architecture, code, and documentation at handoff.

Outcomes

What an audit or build moves.

Concrete results from real engagements across Snowflake, AWS, and dbt stacks.

~50%
Pipeline runtime reduction — same data, less compute
12+
Years production data platform experience at Sonos, Amazon, Meredith
8–16
Typical audit hours, agreed cap before I start
✓Snowflake spend brought under control and explained
✓Alert coverage before stakeholders notice a failure
✓Schema and access changes that don't escape review
✓CI/CD that deploys confidently, not carefully
✓Architecture designed to survive the next order of magnitude
✓Documentation you can hand to the next engineer
Supreeth M Gowda
"Forged in California, building in Bangalore."
Connect on LinkedIn →
About

The best data platforms are designed by engineers who understand how they fail.

I'm Supreeth M Gowda — a data platform consultant who has spent 12+ years building production data systems at Sonos, Meredith, Amazon, and Mindera. Most of that time was on small teams where I owned problems end to end: from designing the ingestion architecture to finding out why it broke at 3am. That's the background I bring to every engagement.

Encore is deliberately solo. You get a senior engineer hands-on with your problem — not a sales call followed by a junior doing the actual work. My depth is pipeline architecture: tool selection, orchestration, CI/CD, and the observability that catches failures before your stakeholders do. If the ask is dimensional modeling or schema design, I'll say so upfront — not after you've paid for it.

Selected Experience
  • MinderaData Engineering Consultant · Bengaluru, KA
  • AmazonData Engineer II · Seattle, WA
  • Time Inc (Meredith)Big Data Engineer / Senior SDET · Los Angeles, CA
  • SonosBig Data Engineer / Software Test Engineer · Santa Barbara, CA
Education
  • UC Berkeley (Haas)Professional Certificate, ML & AI
  • WPIMS, Computer Science · Worcester, MA
  • VTUBE, Computer Science · Karnataka, India
Writing

Engineering insights.

Articles on architecture, reliability, performance, and the engineering decisions behind modern data platforms.

Read all posts on the blog →

Your stack deserves a second opinion.

Whether you're fixing what's already running or building from scratch, let's talk. A 30-minute scoping call — no pitch, just an honest read on what it would take.

Book a call →