The full data stack. Warehouses to pipelines to governance.
Half of organizations still run core applications on legacy platforms. Most AI pilots never make it to production, not because the AI didn't work, but because the data wasn't ready. We fix the foundation so everything built on top of it actually holds. Every capability below runs through our Conductor model, with AI woven into every phase. The tools we use are chosen for the problem, not the pitch deck.
Data platform strategy & architecture
The blueprint before the buildout. We map your current data topology, identify integration gaps, score AI readiness, and design a target architecture that fits your business reality, not a vendor's reference diagram.
Warehouse & lakehouse implementation
Snowflake, Databricks, BigQuery, and Redshift. We'll tell you which one your problem actually needs. Then we'll build it: schema design, access patterns, cost optimization, and the data models your analysts will live in for years.
Pipeline development & orchestration
dbt for transformation logic. Airflow, Dagster, or Prefect for orchestration. Fivetran or Airbyte for ingestion. We build pipelines that handle what happens at 2 a.m. when the source API changes its schema without warning.
API & integration layer buildout
The connective tissue between your systems. When the ERP doesn't talk to the CRM, and the CRM doesn't talk to the warehouse, and everyone exports to Excel: that's our starting point. RESTful APIs, event-driven architectures, webhook systems, and middleware that actually works.
Legacy database migration
On-prem SQL Server instances, Oracle databases nobody wants to touch, Access databases that somehow still run critical processes. We've migrated more aging data systems than we can count, and we've learned where the landmines are buried.
Data quality & governance frameworks
Quality checks, lineage tracking, PII detection, access controls, and documentation that people will actually read. Governance lives in pipeline checks, ownership rules, and operating practices that keep your data trustworthy as the organization scales.
Cloud platform migration
AWS, Azure, and GCP. Moving workloads from on-prem to cloud, or from one cloud to another. Infrastructure-as-code from day one so your team isn't hand-configuring EC2 instances six months from now.
Real-time & streaming architectures
Kafka, Kinesis, and Pub/Sub: for when batch processing isn't fast enough. Fraud detection, operational dashboards, IoT telemetry, and live inventory tracking. We design streaming systems that stay reliable when the volume spikes.
Small team, senior throughout, and AI-augmented at every step.
Every data engineering engagement runs on the same structure: 1-2 Conductors who architect, lead, and own the outcome, supported by AI agents for execution and senior specialist data engineers for the complex work. The person who designs your warehouse schema is the same person in every client meeting.
Your Conductor designs the data architecture
Warehouse schema, pipeline topology, integration patterns, and governance model. These decisions are made by a senior engineer-architect who's built data platforms for a decade and can explain the trade-offs between a Snowflake medallion architecture and a Databricks lakehouse to your CFO without losing anyone in the room.
AI agents handle pipeline scaffolding and repetitive data work
dbt model generation, test scaffolding, schema documentation, data profiling, config files, and boilerplate transformations. AI earns its keep on the work that used to consume 40% of a data engineer's week. Every AI-generated pipeline component passes through five automated quality gates: schema validation, data quality checks, performance benchmarks, PII scanning, and deployment readiness.
Senior specialist data engineers handle complex implementation
Advanced dbt modeling, Spark optimization, complex ETL for messy source systems, real-time streaming pipelines, performance tuning for query patterns nobody anticipated. Our engineer bench includes senior data engineers vetted through paid working sessions, each with 5+ years of experience on the specific platforms your project needs.
Clean handoff with full documentation and operational playbook
Pipeline orchestration, monitoring dashboards, data quality alerts, and runbooks are built during construction, not scrawled on a wiki the last week. When the engagement ends, your team owns everything: the code, the infrastructure, and the operational playbook. And the platform is built for what comes next, not just what you need today.
What this model actually looks like in practice.
We're clear about how long engagements take and what kind of team you're working with. Assessments are fixed-scope, fixed-price. Implementations are sprint-based with clear milestones. We'll talk through investment once we understand your data and the scope.
The cost that matters most is the cost of bad data: the hours your team burns reconciling reports by hand, the decisions made on numbers nobody quite trusts, and the billings that slip through because the systems don't agree. A buyer feels it too, discounting a business whose data can't be relied on. A focused platform build is usually smaller than the cost of living with the problem, and it keeps paying back.
We're a good fit when your data problems are real, not hypothetical.
Our best data engineering engagements are with organizations that know something's broken and need someone who's fixed it before. Here's who typically gets the most from working with us.
Companies whose data lives in silos
The ERP doesn't talk to the CRM. The CRM doesn't talk to the warehouse. Everyone exports to Excel and emails it around on Fridays. You need someone who can connect these systems and build a single source of truth that people actually trust.
Organizations preparing for AI
The board wants an AI strategy. But your data is scattered across three databases, two SaaS platforms, and a shared drive full of CSVs. You're smart enough to know that AI without clean data is just expensive guessing. You need the foundation built first.
CTOs inheriting a mess
You just started, and the "data platform" is a tangle of manual ETL scripts, undocumented cron jobs, and a MySQL instance running on a server under someone's desk. Possibly literally. You need a partner who won't flinch when they see what they're working with.
PE portfolio companies
The operating partner needs clean KPI tracking across acquisitions. The 100-day value creation plan includes "data modernization" as a line item. You need a firm that speaks PE, ships on schedule, and can consolidate three acquired companies' data into one coherent platform.
Great fit
- Data scattered across multiple systems with no single source of truth
- Organizations preparing for AI that need the data foundation built first
- CTOs inheriting legacy data infrastructure with no documentation
- PE portfolio companies needing data consolidation across acquisitions
- Companies where analysts spend more time wrangling data than using it
Not the right fit
- Teams that just need a BI dashboard on clean, well-structured data
- Organizations looking for a single CSV-to-database migration
- Companies that want AI without fixing the data first
- Teams needing Salesforce or HubSpot configuration, not custom infrastructure
Not sure where to start? Most of our data clients don't either.
The Data Readiness Assessment is a fixed-scope, fixed-price engagement that audits your data architecture, maps your integration gaps, scores your AI readiness, and hands you a prioritized modernization roadmap with 3-5 high-impact use cases. It's the diagnostic before the treatment.
Most of our platform modernization engagements start here. Some clients take the roadmap and execute it themselves. Most ask us to keep building. Either way, you walk out with clarity you didn't have before.
Explore the Data Readiness AssessmentAlready know what you need?
If you've done the diagnostic and you're ready to build, we can start scoping a platform implementation directly. Every project starts with a conversation, not a contract. Tell us what you're working on.
Data engineering is often where the relationship starts. Almost never where it ends.
Once the data foundation is solid, the opportunities multiply. The same Conductor who built your warehouse sees what becomes possible when the data is clean, connected, and ready. Same person, broader scope, and all the context already in place.