Specialized
Data infrastructure that the people making decisions can actually use
We build end-to-end data estates — from ingestion through to self-service analytics. When the CEO needs a number, they get it in minutes, not by filing a request.
Full stack
Ingestion to self-service analytics
< 100ms
Streaming pipeline E2E latency
Tested
Data quality checks on every pipeline
Version
controlled transformations in dbt
What we build
Every layer of the modern data estate
Data infrastructure is only as good as the decisions it enables. We build for the end user — the analyst, the exec, the operations manager — not just the data engineer.
Modern data stack implementation
Design and deployment of a production-grade data stack: ingestion, warehouse, transformation, and visualization layers. We build on proven components — dbt, Snowflake, Databricks, or whichever warehouse is right for your query patterns — and configure them for your actual workload.
Includes: Architecture design, stack selection, full implementation
Real-time pipeline engineering
Streaming data pipelines that process events in real time — from IoT sensors, transactional systems, application logs, and third-party APIs. Sub-100ms end-to-end latency from source to queryable. No batching where it isn't needed.
Tech: Apache Kafka, Flink, Kinesis, Pub/Sub — based on your stack
Self-service analytics enablement
The most underused investment in most data programs is the semantic layer — the definitions, business logic, and access controls that make analytics accessible to non-engineers. We build it and train your business teams to use it without filing data requests.
Tools: dbt metrics, Cube, LookML, or custom semantic layer
Data governance and cataloging
A functioning data catalog, ownership model, and quality framework — not a governance document that nobody reads. We stand up the tooling, establish the ownership process, and make sure data producers and consumers have a shared understanding of what the data means.
Tools: Atlan, Alation, DataHub, OpenMetadata — based on your scale
Data security and access control
Row-level and column-level security enforced at the query engine layer. PII discovery and classification across your data estate. Access policies that satisfy GDPR, HIPAA, and CCPA requirements — written as code and version-controlled.
Frameworks: RBAC + ABAC · Policy-as-code enforcement
Data quality and observability
Automated data quality checks, freshness monitoring, and anomaly detection across your pipelines. Data incidents are detected before downstream consumers notice them. Quality scores are visible in your catalog — not hidden in monitoring dashboards only engineers watch.
Tools: Great Expectations, dbt tests, Monte Carlo, Soda — integrated into your pipelines
Technology
We're opinionated about the stack, not about the vendor
We work with all major data platforms. Technology selection follows your query patterns, scale requirements, and cost targets — not our partner relationships.
Ingestion
Everything from databases to IoT streams
Storage & Warehouse
Recommendation based on query pattern and cost model
Transformation
Version-controlled, tested, documented
Semantic Layer
The layer that makes analytics self-service
Consumption
Your analysts use the tools they already know
Governance & Catalog
Ownership, lineage, and quality in one place
How we work
From fragmented data to production analytics platform
Most data projects fail because they start with technology and work backwards to the problem. We start with what your business needs to decide.
Data Audit
Week 1–2
We inventory your current data assets — where data lives, how it moves, who uses it, and what quality problems exist. We interview data producers and consumers separately and compare what each thinks the data means.
Data estate audit + consumer needs assessment
Architecture Design
Week 2–4
We produce the target data architecture — stack selection, pipeline design, warehouse schema approach, and access model. Cost modeling is included. The architecture is designed to serve your analysts first, not just to look good on a diagram.
Data architecture design + technology selection + cost model
Foundation Build
Week 4–8
Ingestion connectors, warehouse provisioning, base transformation layer, and access controls are built and tested. The first set of production pipelines runs in staging with full monitoring before it touches production data.
Production-ready data foundation + first pipeline set
Expansion
Week 8–16
Additional data sources onboarded in prioritized waves. Semantic layer built out to cover the use cases your business analysts most need. Dashboard templates delivered. Business teams trained to use the self-service layer.
Semantic layer + dashboards + trained business users
Governance
Week 12–20
Data catalog populated with ownership, lineage, and quality metadata. Data quality checks deployed across every pipeline. Governance process established with your data team so the catalog stays current after we leave.
Live data catalog + quality framework + governance process documentation
Use Cases
Data problems we're built to solve
Retail & Commerce
Moving from weekly reporting cycles to same-day operational intelligence
The Situation
A retailer's analytics team produces a weekly business report that takes three days to compile. The CFO frequently makes inventory and markdown decisions based on 10-day-old data. The data team is a bottleneck — analysts across the business file tickets to get numbers the data team extracts manually.
Our Approach
We redesign the pipeline from ingestion through to a self-service semantic layer. The transformation logic that currently lives in spreadsheets moves into version-controlled dbt models. Business analysts are trained to query the semantic layer directly. The data team shifts from manual extraction to building new data products.
Financial Services
Operationalizing AI on a fragmented data estate
The Situation
A financial institution wants to deploy ML models for credit risk and fraud detection. The models exist in notebooks. The data estate is fragmented across three separate systems with inconsistent schemas, undocumented transformations, and no lineage tracking. The models can't be deployed because the feature engineering pipeline doesn't exist.
Our Approach
We build the data infrastructure the models require before touching the models. A unified feature store is built on top of a consolidated data warehouse. Feature definitions are version-controlled with lineage tracked back to source systems. When the models are deployed, the data feeding them is auditable — which is what the compliance team requires.
Is this right for you?
This is a good fit if you…
- Your data is scattered across multiple systems and no one has a clear picture of the business
- Reports take days to produce and are already out of date by the time they're shared
- Your data engineering team spends more time on manual pipeline maintenance than on new work
- Non-technical staff can't answer their own data questions without filing a request
- You're making significant decisions on incomplete or inconsistent data
You might want to start elsewhere if…
- You just need a simple reporting tool connected to one database — that's a two-hour setup
- You're a very small business with minimal data and a single source of truth that already works
Common questions
Questions people ask before getting started
Plain answers. No jargon. If something isn't covered here, just ask us directly.
Ready to talk data infrastructure?
Tell us where your data lives today and what decisions it's failing to support. We'll assess the gap and tell you what a realistic engagement looks like.