Data Engineering & Data Warehouse
Pipelines, warehouses, and the data foundation your dashboards and models sit on.
Data engineering is the build-and-consulting work that sits under your dashboards and models: the pipelines that move data, the warehouse it lands in, and the modeling that makes a number read the same everywhere. You need a warehouse once analytics has to stitch several sources together, history starts to matter, or reporting queries are dragging on your production database. Below that line, a BI tool reading straight off your app database is usually enough, and we'll say so. This is the plumbing side, separate from the dashboards in our data analytics and BI work and the high-volume streaming platforms in our big data consulting.
How we approach data engineering & data warehouse
Dashboards and models are only as honest as the data underneath them, and that layer is where most analytics work quietly breaks. Data engineering is the plumbing: the pipelines that move data, the warehouse it lands in, and the modeling that makes it queryable and trustworthy. We build that foundation so a number means the same thing in every report and every model that reads from it.
This is the build-and-consulting side of data, not the dashboard. We design and stand up the warehouse, wire the pipelines that feed it, and put the tests and documentation in place so it stays reliable as sources change. When the base is solid, BI and machine learning stop fighting the data and start using it. We're pragmatic about tooling and weigh cost and operability alongside throughput rather than chasing the newest stack.
In every engagement
Scope flexes to the problem, but these are the things you can count on us bringing.
- Data warehouse and lakehouse design and build
- ELT / ETL pipeline engineering and orchestration
- Dimensional modeling and a single source of truth
- Data quality tests, lineage, and documentation
The right warehouse depends on your workload, not the loudest logo
Five honest reads on where each platform fits and where it bites. Pricing models are factual; the dollar amount is yours to model against your own query volume.
| Platform | Best fit | Pricing model | Watch-out |
|---|---|---|---|
| Snowflake | Mixed, bursty SQL workloads across teams that want storage and compute to scale apart. | Per-second compute, billed in credits, with storage priced separately. | Idle warehouses keep burning credits until they auto-suspend. Set that aggressively or the bill creeps. |
| Google BigQuery | Serverless analytics where you'd rather not run a cluster, and spend tracks how much you query. | On-demand by bytes scanned, or flat-rate slots once usage is steady. | An unpartitioned table queried all day scans everything every time. Partition and cluster early, not later. |
| Amazon Redshift | Teams already deep in AWS who want provisioned, predictable capacity next to their other services. | Per-node provisioned (RA3), or per-second serverless billed in RPUs. | Sizing is yours to manage. Under-provision and queries queue; over-provision and you pay for idle nodes. |
| Databricks (lakehouse) | One platform for engineering, streaming, and ML on open table formats, not just BI. | Per-DBU compute on top of your own cloud's VM cost. | More power than a SQL-reporting shop needs. The lakehouse flexibility is wasted if all you run is dashboards. |
| Postgres / single-node (when a warehouse is overkill) | Small data, one or two sources, and reports a read replica can serve without straining the app. | Just your existing database, or a managed Postgres instance. No warehouse line item. | It stops scaling once data outgrows memory or analytics queries start blocking writes. That's your signal to move up. |
Pricing structures reflect each vendor's published model as of June 2026: Snowflake bills per-second compute in credits, BigQuery charges by bytes scanned or via flat-rate slots, Redshift offers provisioned RA3 nodes or serverless RPUs, and Databricks meters compute in DBUs. We're vendor-neutral; the fit, not the brand, drives the recommendation.
When a warehouse build is the right call
Three honest verdicts, including the two where the answer is don't build one yet.
When a BI tool on your app database is enough
One main system, a few months of history, and reports a single analyst runs without bringing the app to its knees. A read replica plus a BI tool answers the questions you have today. A warehouse here is cost and upkeep you don't need yet, so we won't push one on you.
When an in-house data team makes more sense
The pipeline work is steady, tied closely to one product, and central enough that you want the knowledge living on staff. If you can hire and keep data engineers, owning it beats a retainer. Our better role is to design the first version, set the patterns, and hand it over clean.
When warehouse and data-engineering consulting fits
Several sources that disagree, reports stitched together by overnight copy-paste, analytics queries choking the production database, or a model whose training data nobody quite trusts. You want the foundation done right the first time without spending a year learning warehouse engineering by trial. That's the build to bring us.
Questions buyers ask about data engineering & data warehouse
How do I know when we actually need a data warehouse?
Three signals usually settle it. Reports have to combine data from systems that don't talk to each other. Analytics queries are slowing the database your app runs on. Or you need real history because the source systems overwrite it. Hit one or more of those and a warehouse earns its keep. Until then, a BI tool on a read replica is cheaper and simpler.
What's the difference between data engineering and BI or analytics?
Data engineering is the foundation: the pipelines, the warehouse, and the modeling that turn raw source data into something trustworthy. BI and analytics sit on top and turn that into dashboards, reports, and decisions. We do both as separate services, but the order matters. A dashboard built on shaky pipelines just shows wrong numbers faster.
How do you keep a warehouse from turning into another data swamp?
Tests, lineage, and documentation, treated as part of the build rather than cleanup afterward. Every pipeline gets data-quality checks that fail loudly when a source changes shape. Models carry documented definitions so a metric means one thing. Lineage shows where each number came from. The discipline is what keeps the warehouse trustworthy a year in, not just on launch day.
Should we pick the warehouse platform before talking to you?
No. Picking the platform first often locks you into the wrong cost model. The honest choice depends on how spiky your query load is, what cloud you already run, and whether you need plain SQL reporting or streaming and ML too. We start from the workload, walk through the trade-offs in plain terms, and land on a platform that fits. The fit drives the pick, not the brand.
Can you work with the warehouse and tools we already have?
Yes, and most engagements start there. We meet your stack where it is, whether that's Snowflake, BigQuery, Redshift, Databricks, or a Postgres setup that has outgrown itself. We'll tell you straight where the current setup is fine and where it's holding analytics back. A clean fix on what you own usually beats a rip-and-replace nobody asked for.
Industries we know well
The same service, sharpened by the regulations and realities of your sector.
Austin oversight, dual-shore delivery
We run data work the way we run every engagement: a US-based lead in Austin owns the architecture, the modeling decisions, and the review gates, while engineers in our Bangalore and Mohali centers build the pipelines and the warehouse. You sign a US contract with full IP assignment, and the delivery team overlaps three to four hours of your day. Tell us what your data looks like and what's breaking, and you'll get a straight read on scope, cost, and timeline within 48 hours, with a real range in the first conversation.
The proof we point to is our own. Shield Suite is a gmware product that delivers retail intelligence across more than 60,000 beverage-alcohol storefronts, and a system at that scale lives or dies on its data engineering. The pipelines, the warehouse, and the modeling behind it are work we own end to end, not a slide. When we say we build data foundations that hold up under real volume, that's the one we'd show you. Once the foundation is solid, the same team can carry it into AI and LLM work that depends on trustworthy data underneath.
See it on your own data.
Book a 30-minute demo. We'll walk through Shield Suite with your use case in mind.