Data Platform Modernization Services: cost, scope, and how to hire

Engagements start from $500, with the final fixed-bid quote shaped to your platform size and the workflow slice you want to ship first. We agree the full scope and investment on the free discovery call before any work begins — no time-and-materials surprises, no hidden charges. Larger multi-domain or full warehouse migrations, and Embedded Partner retainers, are scoped during the same conversation. For market context, comparable US partner scope runs $300K–$3M one-time and $10K–$40K/month; industry data shows median data platform migration runs $450K–$2.3M, with single-warehouse migrations like Redshift → Snowflake/BigQuery typically $400K–$800K.

First slice ships in 6–10 weeks. The new pipeline runs alongside legacy from week 5 onward, so reconciliation and trust-building start before cutover. Full multi-domain modernization runs 3–9 months, with each slice shipping value independently. Staged delivery means the business sees outcomes at the end of each slice, not at the end of the program.

Yes, this is the entire point of staged modernization. The legacy pipeline runs in parallel with the new one until reconciliation matches for an agreed window — monitored, and trusted by the team that owns the numbers. Cutover happens after the numbers match, not on a calendar date.

Default is dbt + BigQuery + Airflow on GKE for warehouse and orchestration, with Cloud Run, Lambda, or dlt for lightweight ingestion. Snowflake, PostgreSQL, Redshift, Databricks, and Prefect are all in scope when they fit the workflow. Stack is chosen by workflow pressure, not by allegiance.

Usually not. Clean contracts, lineage, governed metrics, and observable pipelines are what make a platform AI-ready. If the modernization is done well, the AI agent layer becomes a small additional engagement (often hybrid AI + data scope) rather than a new platform decision.

All three. GCP-strongest (BigQuery, GKE, Cloud Run, Cloud Composer), AWS-strong (Redshift, EKS, MWAA, Lambda), Azure-capable (Synapse, AKS, Databricks). The framework is identical across clouds: workflow-mapped audit, staged migration, dbt modeling, parallel-run cutover, guardrails. Cloud-specific depth is disclosed honestly on the discovery call.

Yes, when structured properly. We work read-access only on production data where possible, no production data leaves your cloud, full audit logs of every action, signed BAA or DPA where applicable, and right-to-audit clauses. We have shipped under SOC2 and GDPR-aligned controls with EU and US healthcare-adjacent clients. HIPAA-covered PHI typically requires a US-based co-vendor for BAA chain reasons; we partner accordingly.

Production Build is fixed-bid, scoped before signing on the discovery call, engagements start from $500, with the final number set transparently before work begins. Embedded Partner is fixed monthly scope with no hourly billing, also agreed during the call. Most clients sign through Upwork (verified reviews, contract setup, payment protection) or direct contracts. We can sign MSA / SOW for procurement-led engagements. We do not do percent-of-savings billing because it creates an incentive to over-cut scope.

Three guardrails ship as part of every Production Build: schema contracts between producers and consumers (so changes are coordinated, not surprises), freshness SLAs with alerting routed to the team that owns the data, and a versioned metric layer so KPI definitions stop drifting. Without these, modernization gains decay within 12–18 months. With them, the platform stays maintainable. The Embedded Partner retainer keeps the program running across multiple slices.

Yes. We're tool-agnostic. About 40% of our buyers keep a managed ingestion SaaS for the easy sources while we ship the harder ingestion, modeling, and orchestration work. The two pair well: managed connectors handle the commodity flow, we handle the workflow-critical paths. We won't recommend tooling you don't need.