Analytics Engineering

Revolutionize your next data product and level up your analytics

What is Analytics Engineering?

Analytics Engineering bridges the gap between data engineering and data analytics. Our analytics engineers provide business-ready data sets and analysis to clients in a more iterative, rapid, and low/no-code format that increases speed to insight without needing to rely on code-heavy IT platforms.

It’s not just data prep. In addition to wrangling even your messiest data, our analytics engineers can help enable spatial, prescriptive, and predictive analytics in a way that is simple for you to analyze and maintain.


  • Automate repeatable analytics processes to save time and money.
  • Rapidly prototype a variety of different data products to determine which solutions are truly feasible.
  • Advance simple mapmaking to unleash profound geospatial intelligence and analytics into your location data.
  • Custom tool development using Python SDK, API integrations, and other customization to take your low-code tools to the next level.


  • Optimize server deployments to keep things up, governed, and running.
  • Simplify your scripts with custom, user-friendly programs that are code-free, and code-friendly.
  • Ensure availability of data products for everyone who needs it.
  • Document data products for easy usage moving forward.


  • Program-based workflow migration to make the right change you need, to the right platform for you.
  • Automated migration requirements gathering products to accelerate the process.
  • Elastic Operations for competitive and rapid results.

Who needs Analytics Engineering?

IT and data management teams that want to enable self-service analytics and ML for business users.

Business teams that want to enable their own insights with simple, low/no-code, maintainable solutions for their analytics needs.

Product owners who want to meet users’ needs fast.

Citizen data scientists who need reliable access to data in all its forms.

Governance and security teams that want to ensure quality and secure data.

What is (and is not) covered in an Analytics Engineering project?

While no two customers are exactly alike, below we list examples of what is typically covered and not covered.

What do Analytics Engineers do?

What do Analytics Engineers not do?

phData’s Analytics Engineering services are trusted by 

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Got Analytics Engineering questions? We've got answers.

Our team has deep experience and expertise in Alteryx, KNIME, Tableau Prep, Power Platform, Matillion, Tableau CRM, DBT, Python, and R.

Data engineering is often concerned with large-scale infrastructure tasks, including data ingestion and warehousing. Analytics engineering is focused on business logic, requires knowledge in how the data is actually used, and is better oriented to understand the underlying business value of their work.

Because of the breadth of capabilities analytics engineering projects can enable, no two projects are alike. Pilots or POC projects can start as low as $20,000 – $30,000. That price will vary based on the complexity of the use case.

How our Analytics Engineers can help lead data-driven business transformation

  • Analytics community leaders

    Our team consists of industry-recognized experts and community leaders. In every analytics engineering project, you know you’re working with an expert with a proven track record.

  • Quicker time to value

    Relying on IT to build out your data processes can take time. Our team can rapidly prototype solutions to get your manual data processes solved quickly, getting to your answers faster and saving you time.

  • Proven best practices

    We’ve developed our approach with the most advanced data and analytics organizations, deploying thousands of data product use cases. 

  • Enablement experts

    We don’t just build data products, we ensure that you feel supported in maintaining them after the project ends. We work to foster a culture around data that will enable you to continuously drive value from your own data.

Take the next step
with phData.

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