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

Your data stack works. Until it doesn't.

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

You have the business problem. You need someone who's built this before.

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.

~/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.

~/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.

Blog · CI/CD

No matter how good AI gets, you still need to get your environments right

An AI-generated DAG still has nowhere safe to run if your environments aren't set up correctly underneath it.

Read →
Blog · Performance

The optimization discipline nobody talks about

12 years of tuning compute across Spark, Glue, and Snowflake — the recurring discipline behind every era of data tooling.

Read →
Blog · AI

Why experience matters most in the AI era

AI hasn't replaced engineering judgment — it's just removed the moment where judgment used to get applied.

Read →
Blog · Risk

Risk-first thinking: catch data problems before they cost you

Spotting the cost and reliability risks in a stack before they turn into incidents — the mindset behind an audit.

Read →
Blog · Migration

Why your warehouse-to-lake migration will fail

The fixes to make before you commit budget — what actually sinks lakehouse migrations.

Read →
Blog · Scale

From 10 TB to 50 PB: building data lakes across three companies

Lessons and bottlenecks from scaling data lakes by four orders of magnitude.

Read →
Blog · Lessons

12 years in data engineering taught me one thing — we keep making the same mistake

The recurring failure pattern behind most stacks that break.

Read →
Blog · Journey

My journey in the big data world — data through my eyes

How I got into big data and what the path taught me along the way.

Read →
Blog · Build

How I built an AI-powered data architecture tool on AWS

The architecture and CI/CD behind DataFoundry — from manual deploys to a one-click pipeline.

Read →
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 →