Cloud and data platform migrations.
Fixed fee, reconciled.
AWS, GCP, Azure, BigQuery, Snowflake, Databricks, Redshift, and the long tail of legacy ETL. Three engineers in India, working with US and EU mid-market teams. Most engagements start with a free 90-min cost review.
Or — if you want the longer conversation — talk to us about architecture, migration, or platform-management work.
Does any of this sound familiar?
If it does, the next section explains how the engagement model is structured to address each one.
Your cloud bill grew 40% last quarter and no one can explain why.
FinOps Quick-Check surfaces the named levers in two minutes. Cost X-Ray turns those levers into a scoped savings model with a 30/60/90-day plan.
You are mid-migration and discovery keeps widening the scope.
A written Appendix A locks the scope before work begins. Anything outside it is a Change Order, priced and signed before work starts — not an invoice surprise.
Your last SI handed the work to a junior bench and disappeared.
The engineer on your first call is the engineer in your repo. No handoffs, no bait-and-switch staffing.
You need evidence the migration actually worked, not a PowerPoint.
Row counts, checksums, query-result diffs, schema parity — all deterministic, all written to a signed reconciliation artefact you keep.
You are on T&M and have no idea what the final number will be.
Every engagement runs against a fixed-fee scope. If we missed something in-scope during assessment, we absorb it — not you.
Your architecture worked at 1× and is falling over at 10×.
Landing zone design, multi-account/multi-region blueprints, and written IaC scaffold — delivered before you scale further, not after the incident.
Cloud and data platform engineering, end to end.
Five productized services. Each one has a written scope, a defined deliverable, and a fixed-fee engagement shape.
Cloud Migrations
AWS, GCP, Azure, OCI. Into the cloud, between clouds, or off a cloud you have outgrown. Landing zones, multi-account designs, FinOps after cutover.
Data Platform Migrations
Warehouse-to-warehouse, database-to-database, and legacy ETL to modern stacks. The team has shipped these pairs end-to-end across prior roles. Reconciled object-by-object.
Cloud & Data Architecture
Landing zones, multi-account and multi-region design, security baselines, lakehouse architecture, DR/BCP, observability, CI/CD blueprints. Written architecture, IaC scaffold, 12-month roadmap.
FinOps & Cost Reduction
Compute rightsizing, RI/SP planning, idle-asset cleanup, S3 tiering, egress optimization, Kubernetes spot strategy, anomaly detection, allocation. Public benchmarks (Flexera, FinOps Foundation) show 15-30% recoverable spend on commodity compute via rightsizing alone.
Cloud Platform Management
24×7 monitoring, IAM governance, patching, backup and DR, security posture management, compliance management, incident management, capacity and SLO tracking. Named senior engineer, not an MSP ticket queue.
Engineering-as-a-Service
Dedicated cloud-engineering capacity for digital-natives and cloud-natives. Embedded engineers, fractional staff engineer, platform team augmentation. Fixed monthly capacity, senior talent, ship-not-staffed posture.
One discipline, every engagement.
Every migration runs the same six-stage pipeline: Inventory, Plan, Convert, Validate, Reconcile, Report. Human sign-off gates enforce the stage boundaries that matter.
Six pairs the rule library covers.
These migration shapes have been shipped end-to-end across prior roles — each one carries a codified rule library and a reconciliation harness specific to it. Other shapes are quoted case by case.
Bespoke shapes — Teradata → Databricks, cloud-to-cloud (AWS ↔ GCP ↔ Azure), database-to-Postgres, on-prem to managed-warehouse — are quoted case by case. Tell us the shape and we will say what is in scope and what is not before any commitment.
The rest of the market vs. what we do differently.
Every promise in this table lives in the contract, not the pitch deck.
| Dimension | Traditional T&M SI | Replatform |
|---|---|---|
| Pricing model | Time & materials — final cost unknown at project start | Fixed-fee against a written Appendix A — no surprises |
| Staffing | Senior pitched, junior delivered — bench economics drive the swap | The engineer on the first call is the engineer in the repo |
| Validation | Verbal sign-off or sampling — "it looks right" | Deterministic row counts, checksums, query diffs — signed artefact you keep |
| Scope creep | Absorbed into T&M — change orders often verbal, billed later | Anything out-of-scope is a written Change Order before work begins |
| Timeline | Months of ramp, discovery, re-discovery, re-scoping | Fixed Discovery Sprint produces inventory + wave plan before you commit to execution |
Start small. Scale on evidence.
One free tool, one free cost review, a scoped sprint, and then execution. Every step is designed so you can stop at any point and take the output elsewhere — though most teams don't.
FinOps Quick-Check
Six questions. Runs in your browser. Returns a monthly waste range and the named levers to investigate — platform-aware across AWS, GCP, Azure, Snowflake, Databricks, and BigQuery.
Run the Quick-CheckCloud Cost X-Ray
A live working session on your warehouse bill. You leave with named levers, a savings model, and a 30/60/90-day plan. After Q3 2026 it reverts to a $100 paid diagnostic, credited 100% to any execution engagement signed within 60 days.
Send me your billDiscovery Sprint
A structured Discovery Sprint produces a full inventory, wave plan, and effort estimate before you commit to execution. Scoped per engagement. Output is a written assessment report — yours to keep.
Start with a Discovery SprintMigrations, Architecture & More
Full migrations, execution waves, architecture engagements, FinOps audits beyond the warehouse, platform-management retainers, engineering pods. Quoted against a written scope after a discovery conversation.
Talk to usThe people doing the work.
Cloud and data platform engineer with 6+ years across migrations, lakehouse architecture, FinOps, and managed platform operations on AWS, GCP, Azure, Snowflake, Databricks, BigQuery, and Redshift. The engineer on your first call is the engineer in your repo — no bench hand-offs, no junior substitutions.
Not ready for a call?
Run through the Migration Readiness Checklist first — 20 items that map to the six pipeline stages. Count the gaps and score yourself before committing to an engagement.
Unhappy with your platform bill?
Run the FinOps Quick-Check first — two minutes, no email, gives you a monthly waste range and the named levers to investigate.