Services
Data Engineering
Reliable data starts with reliable infrastructure. We design and build the pipelines, warehouses, and integrations that move your data from scattered sources into systems your analysts and decision-makers can trust.
Key Takeaways
Data engineering builds the pipelines, warehouses, and infrastructure that make your data reliable, accessible, and ready for analytics and AI. Without solid engineering, every downstream analysis is built on shaky ground.
- Modern data pipelines that consolidate data from 10+ sources automatically
- Cloud data warehouse implementation on Snowflake, BigQuery, or Redshift
- Automated data quality checks that catch issues before they reach dashboards
- ETL/ELT pipeline design that scales with your data volume
- Infrastructure that supports both current analytics and future AI workloads
The infrastructure everything else depends on
Analytics, BI, AI, automation — none of it works without reliable data infrastructure underneath. Data engineering is the foundation: the pipelines that move data, the warehouses that store it, and the integrations that connect your systems.
When the foundation is solid, dashboards load fast, metrics match, and new data sources take days to integrate instead of months. When it's not, every project downstream takes 3x longer and produces results nobody trusts.
We build data infrastructure that is:
- ✓Reliable. Automated pipelines that run on schedule, handle failures gracefully, and alert your team when something needs attention.
- ✓Scalable. Architecture designed for your current volume with room to grow. Adding new data sources, users, or use cases shouldn't require a rebuild.
- ✓Documented. Every pipeline, every transformation, every business rule — documented so your team can maintain it without tribal knowledge.
What we build
Data Pipelines & ETL
Automated extract, transform, and load processes that move data from source systems to your warehouse on a defined schedule. We build pipelines that are reliable, observable, and maintainable.
Cloud Data Warehouses
Design and implementation on Snowflake, BigQuery, Redshift, or Databricks. We choose the platform that fits your stack, your budget, and your team's capabilities.
Data Integration
Connect your CRM, ERP, marketing platforms, operational systems, and third-party APIs into a single, governed data layer. We work with tools like Fivetran, Airbyte, and custom connectors.
Orchestration & Monitoring
Pipeline scheduling, dependency management, and alerting using tools like dbt, Airflow, or Dagster. When something breaks, you know immediately — not at month-end.
Data Modeling
Dimensional models, semantic layers, and business-logic transformations that turn raw data into analytics-ready datasets. Built for both performance and clarity.
Migration & Modernization
Move from legacy databases, on-premise warehouses, or spreadsheet-based workflows to modern cloud infrastructure — without losing historical data or disrupting operations.
Modern stack, right-sized for you
We work with modern, proven technologies — but we don't prescribe a stack. The right tools depend on your existing environment, your team's skills, and the complexity of your data landscape.
Common technologies in our engagements:
- ✓Cloud platforms: Snowflake, Google BigQuery, Amazon Redshift, Databricks, Azure Synapse.
- ✓Transformation: dbt (data build tool) for version-controlled, testable SQL transformations.
- ✓Ingestion: Fivetran, Airbyte, or custom Python pipelines depending on source complexity.
- ✓Orchestration: Apache Airflow, Dagster, or dbt Cloud for scheduling and dependency management.
- ✓Monitoring: Built-in alerting, data quality checks, and observability so you know when something's off.
A mid-market organization with 5-15 data sources doesn't need the same architecture as a Fortune 500 company. We build for your scale — not for an architecture diagram.
Frequently Asked Questions
What cloud platforms do you work with?
We work across all major cloud platforms: Snowflake, Google BigQuery, Amazon Redshift, Databricks, and Azure Synapse. Our recommendation depends on your existing infrastructure, budget, and team skills — we're not locked into any vendor.
How long does a data warehouse build take?
A focused data warehouse project connecting 3-5 source systems typically takes 8-12 weeks. Larger projects with 10+ sources, complex transformations, and multiple downstream consumers may take 3-6 months. We deliver in phases so you see working infrastructure early.
Can you work with our existing infrastructure?
Yes. We often enhance or modernize existing data infrastructure rather than replacing it entirely. If your current warehouse works but your pipelines are fragile, we can rebuild just the pipelines. We assess what exists and recommend targeted improvements.
What's the difference between ETL and ELT?
ETL (extract, transform, load) transforms data before loading it into the warehouse. ELT (extract, load, transform) loads raw data first, then transforms it inside the warehouse. Modern cloud warehouses are powerful enough to handle ELT, which is faster and more flexible. Most of our new builds use an ELT approach.
Do you handle ongoing maintenance?
Yes. We can build the infrastructure and hand it off to your team, or we can provide ongoing maintenance and development through a Data Team as a Service model. Either way, we build with maintainability in mind — documentation, testing, and monitoring are included, not optional.
Related reading
How to Build a Data Strategy: A Practical Guide — data engineering is one step in a broader data strategy. Here's the full framework.
When to Hire a Data Team: Build, Buy, or Blend — deciding between an internal data engineering team and an outsourced model.
See This Service in Action
Building a Data-Driven Foundation for a Nationwide Electrical Contractor
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Predictive Parts Recommendation for a National Appliance Retailer
How VisionWrights built a predictive model that translates customer issue descriptions into likely parts needed — improving first-time-complete repair rates.
Consolidating 15+ Systems into a Modern BI Platform for a $7B Real Estate Firm
How VisionWrights helped a $7B real estate company move from retrospective reporting to real-time visibility by consolidating more than 15 systems into a centralized analytics platform.
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