Data & Analytics

Data Engineering & Analytics

Reliable pipelines, a warehouse that tells one story, and dashboards people actually open on Monday morning.

Most companies do not have a data problem — they have a plumbing problem. The numbers exist, but they live in five systems that disagree, arrive late and break silently. We build the pipelines, models and warehouses that turn that mess into a single, documented source of truth.

Engagements start by working backwards from the decisions you need to make, then designing the ingestion, transformation and reporting layers to serve them — with tests, lineage and monitoring so a broken source is caught by us, not by your CFO.

The first deliverable is always one end-to-end slice: a single source flowing through pipeline, model and dashboard, in production, within weeks. It answers a real question early, proves the architecture on your actual data, and gives every later conversation a working reference — which beats a quarter of platform-building on faith.

Trust is the real product of data work, and it is built mechanically: metrics defined once in a documented model, tests that fail loudly when a source drifts, and lineage that shows exactly where a number came from. When finance and product pull the same figure and get the same answer, the arguments about whose export is right simply stop.

What's included

Data Pipelines & ELT

Orchestrated ingestion from apps, databases and APIs, with retries, backfills and alerting.

Warehouse & Lakehouse Design

Dimensional and lakehouse models on Snowflake, BigQuery or Databricks that stay queryable at scale.

Streaming & Real-Time Data

Event pipelines on Kafka and Spark for operational dashboards and live decisioning.

BI Dashboards & Reporting

Metric layers and dashboards in Looker, Power BI or Metabase — one definition of revenue, not six.

Data Quality & Governance

Tests, freshness checks, lineage and access controls so trust in the numbers survives growth.

Technologies we reach for

  • Airflow
  • dbt
  • Snowflake
  • BigQuery
  • Kafka
  • Spark

Why teams choose us

One Version of the Truth

Metrics defined once in a modeled layer — finance, product and sales stop arguing about whose export is right.

Breakage You Hear About First

Freshness and quality tests on every source mean bad data is caught in the pipeline, not in a board deck.

Costs That Stay Predictable

Partitioning, incremental models and warehouse sizing tuned so compute spend tracks value, not accidents.

Analysts Unblocked

Documented, tested models your analysts can query directly — fewer tickets to engineering, faster answers.

Industries we serve

  • SaaS
  • Fintech
  • Retail & E-commerce
  • Healthcare
  • Logistics
  • Manufacturing

Case study · Retail

One source of truth for a multi-channel retailer

Next-day Reporting cadence · 3 → 1 Sources of truth · ~90% Less manual prep

Read the case study

From the blog

How we work

From concept to launch

01

Discovery & Strategy

Requirements gathering, technical feasibility and architecture planning — we define the fastest path to measurable outcomes.

02

Agile Development

Sprint-based design and engineering with continuous integration and daily communication. No bloat — rapid, transparent, iterative delivery.

03

Delivery & Support

Rigorous QA, smooth deployment, performance monitoring and ongoing maintenance — a product engineered to grow.

Frequently asked questions

Do we need a warehouse, or is our database enough?

If reporting queries are slowing your production database, or answering a question means joining exports from three tools, a warehouse pays for itself. Below that, we will tell you to keep using PostgreSQL and save the money.

What does a data platform cost to build?

A first production pipeline plus warehouse and core dashboards typically runs $7k–$20k; larger platforms with streaming and governance range $20k–$45k+. Warehouse compute is billed by your vendor, and we size it with you up front.

How long before we see our first dashboard?

Usually 3–6 weeks. We ship one end-to-end slice — a single source through pipeline, model and dashboard — before broadening, so you get a working answer early instead of a six-month platform build.

Do we need real-time data?

Rarely, and it costs noticeably more to build and run. Most business questions are answered fine by hourly or daily batches. We recommend streaming when the decision itself is real-time: fraud checks, live ops, alerting.

Can you work with the stack we already have?

Yes. We are vendor-neutral across Snowflake, BigQuery, Databricks and Redshift, and will extend a working setup rather than sell you a migration you do not need.

Our reporting lives in spreadsheets — where do we even start?

That is the most common starting point, and the spreadsheets are useful: they show exactly which numbers the business runs on. We move those definitions into a modeled, tested layer first, keep the spreadsheet interface where people like it, and retire the manual copying rather than the habits.

Who maintains the platform after you build it?

Designed-for-handover is the default: dbt models and pipelines your analysts can read, documentation generated from the code, and training sessions during the build rather than a dump at the end. Teams without data engineers keep us on a light retainer; teams with them take the keys.

Does this prepare us for AI, or is that separate?

It is the same foundation. Every AI use case — forecasting, copilots, retrieval over documents — stands on clean, documented, queryable data. Building the platform around your decisions today is what makes the AI conversation next year an integration, not an excavation.

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