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DaVinci AI
Services · Data Engineering

The platform analytics deserves.

Modern, governed data platforms that turn one-off reports into durable, auditable assets for the whole organization.

Lakehouse & warehouse

Pick the right shape, not the loudest one.

Lakehouse, warehouse, or a pragmatic hybrid, the right architecture depends on your data, your team and your regulatory posture. We help you pick the shape that matches your workload, not the slide deck of the moment.
  • Snowflake, Databricks, BigQuery, Synapse
  • Layered storage (bronze / silver / gold)
  • Cost modeling and right-sizing
Pipelines & quality

Pipelines as products, not scripts in a folder.

We treat pipelines as products: versioned, tested, observed and owned. The result is a data estate where a downstream metric breaking is rare, and when it does, you know within minutes, not at the Monday meeting.
  • dbt, Airflow, Dagster, Azure Data Factory
  • Data contracts and schema evolution
  • Quality tests, freshness SLAs, observability
Governance & access

Governance that enables, not gates.

Good governance is invisible, it makes the right thing the easy thing. We design role-based access, lineage and policy systems that protect what needs protecting while letting analysts move at speed.
  • Role-based access and row/column-level security
  • Lineage, glossaries and data catalogs
  • PII tagging, masking and retention policies
Platform capability map

The full data platform surface.

A complete data platform is the sum of small, well-chosen pieces. Below, what we typically deliver, and the trade-offs behind each layer.

Ingestion

  • Managed SaaS connectors (Fivetran, Airbyte)
  • CDC for operational systems
  • Streaming via Kafka / Event Hubs
  • API and webhook ingestion

Storage

  • Lakehouse with Delta / Iceberg / Parquet
  • Warehouse design (Snowflake, BigQuery, Synapse)
  • Bronze / silver / gold layering
  • Cost modeling and right-sizing

Transformation

  • dbt models (staging, intermediate, marts)
  • Tests, snapshots and macros
  • Incremental and partitioned models
  • CI/CD for analytics code

Orchestration

  • Airflow, Dagster, Prefect, ADF
  • SLAs and freshness monitors
  • Backfill and replay tooling
  • Cross-pipeline dependencies

Governance

  • Role-based access and row/col security
  • Lineage and data catalogs
  • PII tagging, masking and retention
  • Data contracts between teams

Observability & cost

  • Freshness, volume and schema alerts
  • Query cost and warehouse autoscale
  • Data quality dashboards
  • Incident runbooks and on-call
Case snapshot

How it plays out, in practice.

A representative engagement, described in the structure of challenge, approach and outcome. Specifics changed to preserve client confidentiality.

Analytics Modernization
Enterprise

Analytics Modernization

Challenge

A multi-business-unit enterprise was running its reporting on a legacy on-prem BI estate. Report turnaround was measured in weeks, the analyst team was firefighting daily, and trust in the numbers was eroding.

Approach

  • Mapped the active reports and the small set that actually drove decisions
  • Stood up a Snowflake + dbt + Looker stack alongside the legacy estate
  • Migrated the active set in priority order, parallel-running for confidence
  • Decommissioned the legacy reports only after each replacement was verified

Outcome

Report turnaround moved from weeks to hours. Trust in the numbers was rebuilt through documented metric definitions. The analyst team got back enough hours to start shipping the deeper analysis they’d been deferring for two years.

How we partner

Three formats. All senior-led.

Most engagements start with a Discovery sprint, then graduate to a Build sprint or Embedded team. We’re happy to start anywhere that fits the work.

012–4 weeks

Discovery sprint

A focused engagement to define the decision worth informing and prove the data exists to inform it. Ends in a working prototype, an honest feasibility read, and a costed roadmap.

Typical deliverables

  • Decision and KPI map
  • Data feasibility assessment
  • Working prototype on your data
  • Costed roadmap to production
028–12 weeks

Build sprint

A senior pod takes a defined initiative from prototype to production-grade system, designed for your stack, instrumented for adoption, hardened for the real world.

Typical deliverables

  • Production-grade build
  • CI/CD, monitoring and runbooks
  • Stakeholder training and enablement
  • Ninety-day adoption review
03Quarterly

Embedded team

For organizations standing up an internal capability, we embed alongside your team, shipping production work while transferring practice, patterns and ownership.

Typical deliverables

  • Quarterly outcomes plan
  • Pair-building and code review
  • Standards, templates and playbooks
  • Capability transfer and handoff
Frequently asked

Questions we hear, answered honestly.

Should we be on a lakehouse or a warehouse?
Most mid-sized organizations are best served by a warehouse-first approach (Snowflake, BigQuery, Synapse) with a small lake for raw data and ML workloads. Lakehouse pure-plays make sense at larger scale or where heavy unstructured / ML workloads dominate. We’ll model the actual cost and effort for your case before recommending either.
Is dbt the right transformation tool?
For SQL-based analytics transformation, yes, for most teams, most of the time. It’s mature, well-supported and brings discipline that’s hard to replicate elsewhere. We’ll use SQLMesh or alternatives when a specific constraint argues for them.
How do you handle data quality?
Quality tests at every layer (schema, volume, distribution, business rules), freshness SLAs, alerting wired to on-call, and a culture where broken tests block merges. We also instrument data products with usage analytics so dead pipelines get pruned, not patched indefinitely.
Can you migrate us off our current vendor?
We’ve done it more than once, typically incrementally, with the old and new estates parallel-running until trust transfers. We’re vendor-agnostic; the goal is to land on whatever stack fits your team, not to push a favorite.
What about real-time?
Most things that “need real time” need fresh, not real time. We default to a few-minutes-stale architecture for analytics and reserve true streaming for use cases that genuinely require it, fraud detection, operational alerting, customer-facing personalization. The simpler pattern fails far less often.
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Have a problem worth solving?

Whether you’re scoping a new initiative, modernizing analytics, or evaluating where AI actually fits, we’d be glad to talk.

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