The full data
stack, handled.
From messy sources to clean dashboards, we build the data foundation your business can actually make decisions on.
Data Pipelines
Reliable ETL and ELT with dbt and Airflow that ingest, clean, and transform data on schedule, and tell you when something breaks.
Cloud Warehouses
Snowflake, BigQuery, and Redshift modeled for performance and cost, with a single source of truth your whole org shares.
BI & Dashboards
Power BI and Tableau dashboards designed around real decisions, not 200 charts no one opens.
Data Modeling
Well structured, documented data models so metrics mean the same thing in every report and team.
Quality & Governance
Testing, lineage, access controls, and documentation that make your data trustworthy and compliant.
Real Time & Streaming
Streaming pipelines for live operational metrics when daily batches are not fast enough.
The data problems that actually walk through our door.
Nobody calls a data engineering firm because they love pipelines. They call because a number was wrong in a board meeting, a bill doubled, or an AI project ate itself on messy data. Here is how each of those stories goes when it goes right.
Escaping Spreadsheet Reporting
Somewhere in your company, one person spends every Sunday night pulling CSV exports into a spreadsheet so Monday's numbers exist. We replace that ritual with automated pipelines and dashboards that update themselves, so the person who owned the spreadsheet gets their weekends back and the business gets numbers that do not depend on one heroic individual staying employed.
When Reports Disagree
Sales says revenue was one number, finance says another, and the meeting derails into an argument about whose export is right. We fix this at the root: one warehouse, one modeled definition per metric, documented and tested, feeding every report. When the numbers come from ERP, CRM, and product systems that each tell a different story, reconciling them into a single source of truth is the whole job.
Getting Your Data Ready for AI
Every company that rushed an AI pilot in the last two years learned the same expensive lesson: models are only as good as the data underneath them. Retrieval systems hallucinate on stale records, and AI agents make confident decisions on wrong numbers. We build the governed, documented, quality tested data foundation that AI initiatives actually require, including turning contracts, invoices, and PDFs into structured data through document AI. If AI is on your 2026 roadmap, this layer is not optional and it is best built before the pilot, not after the postmortem.
Operational Dashboards for Running the Business
Not board slides. The live screens your operations actually run on: orders in flight, stock levels, jobs on the floor, cash position today. We build real time and near real time dashboards on top of the systems that run your operations, designed with the people who will stare at them every day.
Taming a Runaway Warehouse Bill
Snowflake or BigQuery started at a few hundred a month, and now it is a five figure line item nobody can explain. We audit query patterns, kill idle compute, restructure the models that force full table scans, and set the monitors that catch the next runaway before finance does. Most of these engagements pay for themselves inside a quarter.
Embedded Analytics for Your Product
When your customers start asking for dashboards inside your product, analytics becomes a feature, and features have different standards: tenant isolated data, fast queries at concurrency, and charts that look like your product rather than a bolted on BI tool. We build embedded analytics for SaaS platforms, from the data model to the rendered chart.
You Probably Do Not Need a Data Team of Five.
A full in house data team runs $400,000 to $800,000 a year, and industry research shows data engineers spend about half their time just keeping existing pipelines alive. Most mid market companies are better served by a fractional model: we build the platform, run the pipelines, and hand your team the dashboards, at a monthly cost closer to one junior hire than a department.
Every Pipeline Is an Integration With Consequences.
A data pipeline is an integration where silent failure means executives making decisions on stale numbers. That is why our pipelines borrow the discipline of our API and integration work: contracts, tests, retries, and alerting, so when a source system changes, you get a notification, not a wrong quarter.
From scattered to single source of truth.
We build your data platform in layers, sources first, then trust, then the dashboards leadership relies on.
Audit & Map
We inventory your data sources, current reports, and the questions the business actually needs answered.
Build the Foundation
We stand up the warehouse and pipelines, model the core data, and bake in quality testing from day one.
Visualize
We build dashboards and self serve analytics around real decisions, validated with the people who will use them.
Govern & Scale
We add governance, documentation, and access controls, and train your team to extend the platform themselves.
What data projects cost, and when they are not worth doing.
Data consulting has a reputation for eighteen-month projects that produce a diagram. Here is how we price and phase this work so value shows up in weeks, with the honest numbers first.
A focused pipeline project connecting two or three sources into a warehouse with core dashboards starts around $15,000 to $30,000. A full mid market data platform, meaning warehouse, modeled data, quality testing, and the dashboard layer, typically runs $70,000 to $200,000. Enterprise platforms with dozens of sources, streaming, and strict governance go beyond that. The main cost drivers are the number and messiness of your sources, not the dashboard count, which is why the audit comes before the quote.
Weeks, not quarters, or the project is being run wrong. We phase every build so the first pipelines and a working dashboard on your highest value question ship in 4 to 8 weeks, then the platform grows source by source. A complete mid market platform takes 3 to 6 months end to end, but you should be making decisions on real data long before the last pipeline lands.
Run the math first. One good data engineer costs $150,000 to $200,000 fully loaded, and one is rarely enough, because pipelines need coverage when that person is on holiday, and research shows about half of a data engineer's time goes to maintaining what already exists. A fractional engagement gives you a senior team for a fraction of one salary. The crossover point is real: when data work becomes multiple full time roles worth of new development, hire, and we will help you hand over. Until then, renting the team is usually the better spend.
Two layers, and be suspicious of anyone who only quotes the first. Tooling and warehouse compute typically runs a few hundred dollars a month for small setups, $1,000 to $10,000 for mid size companies, and beyond for heavy workloads. Then the hidden layer: egress fees, idle compute, and unoptimized queries that quietly burn credits. We design with cost monitors, auto suspend, and query governance from day one, and we put a projected monthly run cost in the proposal so the second invoice is never a surprise.
Yes, and that is a design goal, not a courtesy. Everything is built on standard, hireable tools, documented as it is built, and versioned in your repositories and your cloud accounts. The final phase of every engagement is training your people to add sources and build dashboards themselves. Plenty of clients keep us on a small retainer for the plumbing, but that is a choice, never a hostage situation.
More often than this industry admits. If you have one or two clean sources and your questions fit inside your accounting tool or a $100 a month BI subscription, buy the tool and skip the consultants. A custom data platform earns its cost when sources multiply, numbers stop matching, reporting eats real staff hours, or an AI initiative needs a foundation. Industry benchmarks put warehouse ROI around 400 percent over five years with payback inside a year, but those returns only exist where the pain already does. Part of our audit is telling you which side of that line you are on.
Bring the report you trust least. Tracing where its number comes from is the fastest way to see what your data platform should look like, and the audit costs nothing.
Questions about
Data Engineering & Analytics
Both are strong. Power BI fits Microsoft centric orgs and budgets well; Tableau excels at rich visual exploration. We recommend based on your stack, skills, and licensing, and build for both.
If reports come from spreadsheets and conflicting systems, yes. A warehouse gives you one governed source of truth so every team’s numbers finally agree. We right size it to your scale.
Absolutely. We integrate with the databases, SaaS apps, and BI tools you already have, and adopt modern stack components (dbt, Airflow, Fivetran) only where they add real value.
Automated data quality tests, lineage tracking, and clear documentation. When a number looks off, you can trace exactly where it came from, and trust the ones that look right.
Yes, a clean, governed data foundation is exactly what AI needs. The platform we build doubles as the feature and training data layer for our AI work.
Stop guessing.
Start building what works.
Book a free discovery call. We'll map your needs, scope the work, and give you an honest plan, timeline, cost, and trade offs included.
info@croncore.com