Cloud & Data
Data Engineering
Lakehouse architectures, streaming pipelines, and governed data products — the foundation every AI and analytics ambition in your organization is quietly waiting on.
Overview
Why it matters
Ask why an AI initiative stalled or a dashboard is distrusted, and the answer is almost always the same layer: the data. Sources scattered across dozens of operational systems, pipelines that break silently, three departments with three different revenue numbers, and a warehouse designed for last decade's questions. Analysts spend most of their time wrangling instead of analyzing; models train on features nobody can reproduce. Data engineering is the unglamorous discipline that fixes this — and it is the highest-leverage investment in any data or AI strategy.
We build modern data platforms on the lakehouse pattern: open table formats (Delta Lake, Iceberg) on cloud object storage, medallion-layered refinement from raw to business-ready, with engines chosen per workload — Databricks and Spark for heavy transformation and ML, Snowflake or BigQuery for warehousing, Kafka and Flink where minutes-old data isn't fresh enough. Transformations live in dbt or Spark as versioned, tested, documented code — because pipelines without tests are outages on a schedule.
Just as critically, we make data trustworthy and findable: quality checks that block bad data before it propagates, lineage that answers 'where did this number come from?', catalogs and access controls that let you share data safely, and data products with owners and SLAs instead of tables with folklore. That governance layer is what turns a platform into a foundation your AI programs can actually build on.
Business challenges
The problems this practice exists to solve
AI ambitions on a fragile foundation
The GenAI roadmap assumes clean, accessible, well-governed data. What exists is nightly batch jobs into an overloaded warehouse, undocumented pipelines, and features nobody can reproduce.
Three versions of every number
Finance, sales, and operations each compute 'revenue' differently. Executive meetings open with reconciliation debates instead of decisions — and trust in all reporting erodes.
Pipelines that fail silently
A schema change upstream, a null flood, a stalled job — and dashboards quietly serve stale or wrong data for days before anyone notices. Detection by embarrassment.
Data locked in operational silos
Customer truth split across CRM, ERP, support, and product systems with no unified view — making personalization, churn modeling, and even basic cross-sell analysis impossible.
Our solution
How we engineer it
We design the platform backward from your decisions and use cases, not forward from the tools. The architecture is lakehouse-based and deliberately boring where boring wins: open table formats on object storage so you're never locked into one engine, medallion layers (raw, cleansed, business-ready) so every dataset has a defined quality contract, and ingestion via CDC and streaming where freshness matters, efficient batch where it doesn't. Semantic-layer metrics are defined once, centrally — ending the three-versions-of-revenue problem at its root.
Engineering discipline is what makes it durable. Transformations are code: version-controlled, peer-reviewed, tested in CI (dbt tests, Great Expectations checks), and deployed through pipelines like any other software. Data contracts formalize expectations with source systems so upstream changes break loudly in staging instead of silently in production. Orchestration (Airflow/Dagster) gives every pipeline observability, retries, and alerting — mean-time-to-detect for data incidents drops from days to minutes.
Then we operationalize trust: a catalog with ownership and documentation, column-level lineage, role- and attribute-based access control with PII handling built in, and data products published with SLAs to consumers — BI, data science, and increasingly RAG and agent systems that need governed, permission-aware access to enterprise data. Your teams inherit a platform where the next use case is a project, not an excavation.
Capabilities
What data engineering covers
Lakehouse architecture & build
Delta Lake and Iceberg platforms with medallion layering on Databricks, Snowflake, or BigQuery — open formats, engine flexibility, and a defined quality contract per layer.
Real-time & streaming pipelines
Kafka and Flink event streaming, CDC ingestion from operational databases, and exactly-once processing where correctness matters — data fresh enough for the decision it feeds.
Data modeling & semantic layers
Dimensional and wide-table modeling in dbt with centrally defined metrics — one governed definition of revenue, churn, and margin consumed by every tool.
Data quality & observability
Contract tests, anomaly detection, freshness monitoring, and lineage — pipelines that fail loudly in staging instead of silently in production.
Governance & data products
Catalogs, ownership models, access control with PII protection, and data products with SLAs — the operating model that makes data shareable without becoming a liability.
AI-ready data foundations
Feature pipelines, vector-index feeds for RAG, and permission-aware serving for agents — the data layer your AI roadmap actually requires underneath it.
Technology stack
Tools we deploy to production every week
Pragmatic about tools, opinionated about architecture — the platforms below are the ones this practice ships with, chosen per engagement on evidence.
Platforms & Storage
- Databricks
- Snowflake
- BigQuery
- Delta Lake
- Apache Iceberg
- S3 / ADLS / GCS
Processing & Streaming
- Apache Spark
- Kafka
- Flink
- dbt
- Debezium (CDC)
Orchestration & Quality
- Airflow
- Dagster
- Great Expectations
- Monte Carlo
- DataHub / Unity Catalog
Languages & Delivery
- Python
- SQL
- Scala
- Terraform
- GitHub Actions
Implementation process
Five stages. No surprises.
A delivery model refined over 250+ engagements — sequenced so leadership gets visibility and your teams get momentum.
Use-case & source assessment
We inventory the decisions and products the platform must serve, audit source systems and data quality, and define the target architecture with a costed build sequence.
Platform foundation
Lakehouse storage, ingestion framework, orchestration, and CI/CD for data code — plus governance scaffolding (catalog, access model) from the start, not retrofitted.
First data products
Two or three high-value domains modeled end to end — raw to business-ready to dashboard or feature store — proving the platform with visible wins in the first quarter.
Quality & trust layer
Contract tests, freshness and anomaly monitoring, lineage, and incident runbooks — installed while the estate is small enough to instrument completely.
Scale & enablement
Domain-by-domain expansion, self-service patterns for analysts, platform runbooks, and pairing with your engineers until the platform is unambiguously yours.
Use cases
Where enterprises apply it
Customer 360 unification
CRM, ERP, support, and product telemetry resolved into one governed customer entity — the substrate for personalization, churn models, and lifetime-value analytics.
Real-time operational analytics
Streaming pipelines feeding live inventory, logistics, or risk dashboards — decisions made on minutes-old data instead of yesterday's batch.
Legacy warehouse migration
Netezza, Teradata, or on-prem SQL Server estates moved to a lakehouse with reconciliation testing — cutting license cost while unlocking new workloads.
Regulatory & finance reporting backbone
Governed, lineage-complete pipelines for reporting where auditors ask 'where did this number come from?' and the answer must be a query, not a meeting.
ML feature platform
Point-in-time-correct feature pipelines with online/offline consistency — so models train and serve on the same truth.
RAG & agent data serving
Document pipelines, embedding refresh, and permission-aware retrieval layers that let GenAI systems use enterprise data without bypassing its governance.
Outcomes
Results clients report to their boards
92%
reduction in data incident detection time after observability rollout
14
legacy systems unified into one shipment-truth layer for a 40-country logistics network
6x
faster delivery of new analytics use cases on the platform versus the legacy estate
$2.1M
annual savings from legacy warehouse license retirement and compute right-sizing
FAQs
Questions leaders ask us
Direct answers on data engineering — the same ones we give in the first consultation.
Related services
Practices that pair with this one
Ready to put data engineering to work?
In a 45-minute consultation, our architects map your highest-ROI opportunity, outline a delivery plan, and give you a realistic budget range — no obligation.