Services

Data engineering services

We design and build the data backbone your business runs on — so every team can trust the numbers behind their decisions.

Overview

Reliable data, engineered end to end

TecheVision builds the reliable data foundation your analytics, reporting, and AI depend on.

Most data problems aren't really analytics problems — they're engineering problems. When pipelines break, definitions drift, and no one trusts the numbers, every downstream decision suffers. Our data engineering work fixes that at the source: we design and build batch and streaming pipelines, model your warehouse or lakehouse, and put quality checks and observability in place so your data stays accurate and available.

We work across modern cloud data stacks — Python, Apache Spark, Airflow, Snowflake, dbt, and Kafka on AWS and Azure — and we're just as comfortable modernizing a fragile legacy pipeline as building a new platform from scratch. Based in Ashburn, Virginia, we partner with teams across Northern Virginia and remotely nationwide, from startups standing up their first data platform to enterprises scaling theirs.

What we deliver

Everything the engagement can cover

  • Batch & real-time streaming pipelines
  • Cloud data warehouses & lakehouses
  • ETL/ELT design and data modeling
  • Data quality, testing & observability
  • Platform migration & modernization
  • Orchestration & automation
Technologies

Tools we work with

PythonApache SparkAirflowSnowflakedbtKafkaAWS / Azure

Certified, experienced consultants who tailor every solution to your goals — with clear communication and measurable results.

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How we work

A clear path to outcomes

Understand

We start with your goals, systems, and constraints, so what we build solves the real problem — not a generic one.

Engineer

We design and build your pipelines and platform with quality and observability baked in from day one.

Deliver

We hand off working results with clear documentation, and stay available to support and iterate as your needs grow.

FAQ

Common questions

What does a data engineering engagement include?

It depends on your needs, but it typically covers designing and building data pipelines, setting up a cloud warehouse or lakehouse, modeling your data, and adding quality checks so your analytics and ML teams can trust their data.

Which cloud data platforms do you work with?

We build on AWS and Azure and work with warehouses and lakehouses such as Snowflake, along with tools like Apache Spark, Airflow, and dbt.

Can you modernize our existing pipelines?

Yes. We regularly migrate and modernize legacy ETL and data platforms — improving reliability, cost, and speed while keeping your data flowing.

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