Data Engineering

Expert services to design, build, and operationalize your modern data product

What is Data Engineering?

Data engineering teams enable data-driven decision making and machine learning capabilities. They build systems that help you collect, transform, and publish data.


  • Ingest data from internal and external sources, in real-time or batch.
  • Catalog and secure data so that it’s governed and compliant.
  • Monitor and measure data quality and performance.


  • Cleanse your data and increase its value and usability.
  • Enrich datasets through joining, mapping, and feature creation.
  • Model for various data consumers. 
  • Manage workflows for scheduling, audit, performance, and data quality.


  • Analyze via tooling for analytics, machine learning, and AI.
  • Serve data to applications, processes, or people.
  • Govern the catalog of data, dictionaries, and mappings.

Who needs Data Engineering?

IT and data management teams who seek to deliver reliable experience for analytics and ML users.
Business teams who understand the impact of making data-driven decisions.
Product owners who want to monetize data through analytics and machine learning.
Data scientists who need reliable access to data in all its forms.
Governance and security teams who want to enable both innovation and compliance.

Better Data Engineering principles, better outcomes

Failure to recognize the advances of data management will leave you with legacy tools, high costs, poorly developed staff, and ultimately slower business growth (or worse, a declining market position).

How you build your data systems matters. Strong data engineering principles enables you to reliably author, deploy, and support your mission-critical data products.

Data is Software Engineering – Proper source control and build-process automation makes it faster and cheaper to deliver and maintain your code (yes, even SQL).
Iterative approach – Constant feedback loops during development ensure constant progress and alignment with your business needs.
Automate – Data problems can be solved using people or automation. Automation wins through repeatability and preventing inevitable human error.
Build to Operate – Monitoring, logging, operational visibility is critical to business confidence as you get proactive with problem-solving.
Use specialists – Data technologies are both broad and deep: theory doesn’t translate to the real world, expertise and experience do.

We love our Data Engineering clients

Got Data Engineering questions? We've got answers.

phData specializes  in data and machine learning technologies. We have deep experience and expertise in Snowflake, AWS, Azure, Spark, and Hadoop data ecosystems. That said, each of these ecosystems are broad, with various capabilities that we do and do not recommend. As an organization, we’ve built best practices around each of these technologies. Our customers build with us because engineers come with a viewpoint drawn from experience with thousands of data projects.

Design and architecture are often part of our data engineering engagements, but also often a part of data strategy. For complex or large enterprise projects, we typically recommend a dedicated engagement.

No two data engineering projects are alike, but data engineering projects can be as little as $20k-$50k for a POC or pilot. Using our pre-built automation, we’ve implemented several platform builds and legacy migrations for under $100k. That price goes up as the number of sources and requirements increases.

Want to learn more about data engineering? We’ve got you covered.

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Will Your Data Engineering Projects be Successful?

After completing hundreds of data engineering projects, we documented our best practices so you can see if your data engineering projects are set up for success.

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