Pipelines that run, and numbers that match.
Sphere builds the pipelines and integrations behind your reports and AI: sources connected, tests on every load, alerts before anyone notices a gap. An embedded pod ships in 6–12 weeks.
Book a data engineering scoping call
Book a data engineering scoping call
Tell us which systems need connecting and what the data has to power. We'll scope the pod and quote a fixed price.
Your call is with one of oursenior data architectsTechnology partners
Snowflake
Databricks
AWS
Microsoft Azure
Google Cloud
You don't call a data engineer when things work.
Most teams reach out when one of these becomes a weekly argument. Pick yours.
Finance and sales bring different numbers to the same meeting.
The same metric is calculated in three places from three extracts. We build one model everyone reads from.
- One transformation layer with agreed definitions
- Cross-system ID mapping so records line up
- Tests that catch drift before a meeting does
By the time the report lands, the week is over.
Someone exports, cleans and pastes before anyone can look. We automate the path from source to dashboard.
- Scheduled or real-time pipelines
- Automated refresh into Power BI, Tableau or Looker
- Freshness alerts when a source falls behind
Adding a system takes a quarter, not a week.
Point-to-point scripts don't scale. We build an integration layer new sources can plug into.
- Reusable ingestion for CRM, ERP and SaaS tools
- API integrations where connectors don't exist
- A warehouse or lakehouse that can grow
You find out a load failed when a VP asks why the chart is flat.
No tests, no monitoring, no owner. We add the engineering discipline production data needs.
- Data tests on every load
- Monitoring and alerting to the right person
- Runbooks your team can operate
Exports and scripts got you here. They won't get you further.
Most data stacks grew one script and one spreadsheet at a time. That works until the business needs to trust it.
The plumbing behind every report and every model.
Scoped to the sources you have and the decisions the data has to support.
Connectors and APIs for CRM, ERP, finance, SaaS and operational systems.
Scheduled loads and streaming where freshness matters.
Clean, documented models on Snowflake, Databricks or your cloud warehouse.
Schema, freshness and business-rule tests on every load.
Monitoring, lineage and alerts, so failures never reach a dashboard first.
Power BI, Tableau and Looker dashboards, plus governed feeds for AI.
An embedded pod, shipping in weeks.
A small senior team inside your environment, working to a fixed scope.
Book a data engineering scoping call- Weeks 1–2
Source audit & design
We map the sources, volumes and destinations, agree definitions with the business and design the pipeline architecture on your stack.
You getA pipeline design and a fixed scope. - Weeks 2–6
First pipelines live
The highest-value sources are connected, modeled and tested, feeding the first dashboards people actually use.
You getReal data flowing, with tests on every load. - Weeks 6–12
Scale & harden
More sources, monitoring, alerting and lineage, plus the documentation your team needs to run it.
You getProduction pipelines with observability. - Week 12 onward
Handoff or run
We hand over with runbooks and training, or stay on to extend and operate.
You getA data platform your team owns.
Pipelines that shipped.
Data engineering Sphere delivered, from the published case studies.
less data ingestion time
A commercial finance leader sped up underwriting analytics.
Sphere restructured debtor data in the warehouse, standardized client identification across third-party providers and tuned ingestion, indexing, caching and resource allocation on AWS.
Read the commercial finance storyto a governed Snowflake lakehouse and nightly dashboards
A general contractor got one view of every subcontractor.
ERP and field-log data were staged in a Snowflake lakehouse with Fivetran and Python ETL, modeled into a star schema with cross-system ID mapping and refreshed nightly into Power BI.
Read the subcontractor storyregional sites, scaled from a one-plant pilot
A chemicals manufacturer connected the lab, the plant and SAP.
LIMS, MES/SCADA and SAP data were consolidated into Snowflake through Fivetran and dbt pipelines, with standardized master data, SCADA anomaly alerts and role-based Power BI dashboards.
Read the manufacturing storyof manual reporting saved each month (estimated)
Monthly slide decks became daily KPIs.
Customer data platform and SharePoint loyalty data were combined into one Power BI dataset, replacing hand-built Excel and PowerPoint reports with KPI reports that refresh daily.
Read the reporting storyWant results like these?Tell us about your project. A senior architect replies within one business day.
Book a data engineering scoping callFrom the people who signed off on it.
Leaders who trusted Sphere with the data their business runs on.
“Sphere approached AI transformation the right way — starting with workflows, data governance, and operational trust instead of simply deploying another AI tool. Their team built a secure and scalable AI infrastructure that allows us to capture institutional knowledge, protect sensitive data within our environment, and continuously expand our AI capabilities with confidence.”
“Sphere consistently demonstrated a high level of accountability, technical expertise, and executive partnership. Their team remained deeply invested in our business outcomes and successfully supported us through complex technology and operational challenges.”
“Sphere was a valuable partner in helping us advance our AI-native product lifecycle initiatives. Their combination of product thinking, engineering expertise, and execution discipline enabled the delivery of impactful solutions that improved our platform and accelerated innovation.”
Architects who build production data systems.
Your first call is with the people who design Sphere's data and AI platforms, and they stay with the project after you sign.
Built products and led high-scale engineering teams at HP Software, Gett and Google before leading Sphere's technology practices.
Architects enterprise AI platforms that turn complex operations into secure, production-ready systems built to hold up at real-world scale.
Led high-scale operational systems and modernization at Gett and bp pulse. Now builds automation-driven platforms and intelligent workflows.
More than a decade designing and scaling software from first MVP to enterprise-level systems across FinTech, transportation and real-time streaming.
Built on your stack. Owned by you.
On the warehouse and cloud you already pay for.
Code reviews, documentation and lineage from day one.
Runbooks and training so nothing depends on us.
Snowflake
Databricks
AWS
Microsoft Azure
Google Cloud
Salesforce
SAP
NetSuite
Dynamics 365
HubSpotCloud data platforms and the business systems we connect them to.
21 years of engineering. Our top 20 clients stay 7+ years.
Independent recognition for the AI work Sphere delivers, from Clutch reviewers to global AI award juries.
Top Artificial Intelligence Company · US 2025
Top AI Code Generation Company · US 2025
Top Chatbot Company · US 2025
Top Recommendation Systems Company · US 2025
Top AI Text Generation Company · Florida 2025
Top App Development Company · Manufacturing 2025
Global Generative AI Award · 2026 Winner
Best in AI for Documentation Validation · 2026
Best in AI-Powered Invoice Auditing · 2026
Global Recognition Awards · 2026
Global Tech Award Winner · 2026
Most Innovative Technology Consulting Firm · 2026
Data engineering, answered directly.
Don't see yours? Ask it on the call.
Book a data engineering scoping callWhat do data engineering services include?
Ingestion from your source systems, batch and real-time pipelines, transformation into models the business understands, warehouse or lakehouse design, integrations between systems, and the testing and monitoring that keep it all reliable.
Which tools do you use?
Whatever fits your stack. We've built on Snowflake, Databricks, Azure Data Factory, AWS Glue, Fivetran, dbt, Informatica and Python, and delivered into Power BI, Tableau and Looker.
How do you keep pipelines from breaking silently?
Every pipeline gets data tests, freshness checks and alerts that reach the right person. If a source changes or a load fails, you hear about it before the report does.
How long does it take?
An embedded data engineering pod typically ships the first governed pipelines, integrations and dashboards in 6–12 weeks, depending on how many sources are in scope.
Do we have to move to a new platform?
No. We build on the cloud and data platforms you already run, whether that's AWS, Azure, Google Cloud, Snowflake or Databricks. If a migration makes sense, we'll show you why and what it costs before anything moves.
What does it cost?
It depends on the number of sources, volumes and destinations. We quote a fixed price at the scoping call, before any work begins.
Stop reconciling spreadsheets. Let the pipeline do it.
Book a scoping call with a Sphere data architect. You'll leave with:
- Which sources to connect first
- The pipeline design for your stack
- A timeline and a fixed scope for the pod
Book a data engineering scoping call
Tell us about your sources and what the data needs to power. We'll reply within one business day.
Your call is with one of oursenior data architects