Should You Hire a Data Engineer or Outsource?

A full time data engineer costs about $229,000 a year with benefits. Hire when data work is steady. Outsource to build the foundation. Most growing companies get reliable data fastest with a mix of both.

By Maciek Brzakala, CEO of Data Driven. Last updated October 28, 2026.

Short answer. A full time data engineer costs about $229,000 a year in the US once benefits are included. Hire one when you have steady, ongoing data work and someone senior to guide it. Bring in outside help when the work comes in projects, needs several skills at once, or nobody inside knows yet what a good data setup looks like. For most growing companies without a data team, a mix works best. An outside team builds the foundation, and one internal owner runs it day to day.

Many leaders expect their data work to come in waves rather than at a steady pace, and that instinct is often right.

What does a full time data hire cost in 2026?

A full time data hire costs about 1.4 times base salary once benefits are included. Wages and salaries make up 70% of what private employers pay per hour worked, and benefits make up the other 30%, according to BLS employer cost data for June 2026. The table applies that ratio to national salary ranges from Robert Half's 2027 Salary Guide, which projects pay for the coming year. Low reflects a candidate new to the role, and high reflects extensive experience.

Role

Low salary

Mid salary

High salary

Total cost at mid, with benefits

Data engineer

$130,500

$160,500

$185,500

About $229,000

Data architect

$141,000

$172,000

$195,500

About $246,000

Data analyst

$96,250

$118,500

$138,500

About $169,000

Salaries run higher in large metro areas and lower in smaller markets. For a federal comparison, the Bureau of Labor Statistics reports a May 2025 median of $135,980 for software developers, the federal category that includes many data engineers.

What else does hiring cost?

Salary and benefits are only part of the cost. Four more costs come with every hire.

  • Recruiting. The average cost per hire for nonexecutive roles was $5,475 in 2025, according to SHRM's 2025 benchmarking survey. Using a recruiting agency adds a fee based on first year salary.

  • Time to fill. Tech roles in the US and Canada take an average of 56 days to fill, and data scientist roles average 60 days, according to Workable's hiring benchmarks.

  • Time to get productive. A new hire has to learn your systems, data, and business definitions before their work is reliable. Plan on months, not weeks.

  • Coverage. If one person is your only data expert, every report and pipeline depends on them. Leaders often call this the "what if they get hit by a bus" problem.

Why is one data hire rarely enough?

A working data setup needs several skills, and few people are strong in all of them.

Skill

What it covers

How often you need it

Data engineering

Moving data from your systems into one place and keeping it flowing

Heavy at the start, then lighter upkeep

Data modeling

Defining metrics like revenue or utilization so every report agrees

Heavy at the start, then as the business changes

Reporting

Dashboards and self service reports for each team

Ongoing

Security and governance

Who can see what, especially payroll, patient, or customer data

Set up once, reviewed regularly

AI and automation

Tools that answer questions or do work using your data

Project by project

Most companies hire for the skill they feel most, which is usually reporting. That person then spends months on data engineering they were never trained for. Teams whose skills start and end with SQL or advanced spreadsheet functions often stall at exactly this point.

How is outside help priced?

Outside help is usually priced by phase, not by year. A typical engagement for a company with a handful of core systems looks like this.

Phase

What it covers

Typical length

Assessment

Map systems, decisions, and gaps, then recommend a plan

2 to 3 weeks

Foundation build

Connect sources, define core metrics, set up reporting and security

6 to 12 weeks

Ongoing support

Fixes, source system changes, and new reports as the business grows

Ongoing

Cost depends mostly on how many systems are involved and how much reporting is needed. The bigger difference is how the money is spent. A hire is a fixed cost of about $229,000 every year. Outside help is spent in phases, so spending drops once the foundation is built and only support continues.

For a mid market company with a handful of data sources, an outside team is usually the much more cost effective way to get the foundation built quickly. It often stays more cost effective over the long term too, because the ongoing work is mostly upkeep that does not need a full time salary. Most business needs come down to the work in the skills table above, which means integrating data, modeling metrics, building reporting, and keeping it secure. Few companies need the advanced machine learning or data science work that would justify a full time specialist.

When should you hire a full time data person?

Hire when most of these are true.

  • Your teams have enough ongoing data work to fill a full week, every week, not just a one time build.

  • Your data foundation already exists and mainly needs upkeep and new reports.

  • Someone senior can set priorities and review the work.

  • Data is central to what you sell, so the knowledge should live inside the company.

  • You can wait several months to recruit and train a new person.

When does outsourcing make more sense?

Bring in outside help when most of these are true.

  • You need to build or rebuild the foundation, which takes several skills for a few months.

  • Nobody inside can judge whether a data hire is doing good work.

  • Your needs come in bursts, such as an acquisition, a new system, or a reporting deadline.

  • You handle sensitive data and need security set up correctly the first time.

  • You need results in weeks, not after a hiring process.

How do the options compare?

Factor

Full time hire

Outside team

Mix of both

How you pay

About $229,000 a year, fixed, plus recruiting

By phase, with most of the cost during the build

Build cost up front, then ongoing support, plus part of one internal owner's time

Time to start

About two months to fill, then time to get productive

Usually weeks

Usually weeks

Skills covered

One person's strengths

Several specialists

Specialists to build, an owner to run it

Risk if someone leaves

High

Depends on documentation and handoff

Low, because knowledge is written down and shared

Best for

Mature setups with steady work

Building, fixing, or one time projects

Growing companies without a data team

What does the mixed approach look like?

The mixed approach has four stages, and the internal owner stays involved in all of them.

  1. Assess. Map your systems, the decisions leaders need to make, and the gaps between them. This usually takes 2-3 weeks.

  2. Build. An outside team connects your data sources, defines your core metrics, and sets up reporting and security.

  3. Hand off. Your team gets documentation, a data dictionary, and training, so they can run and extend what was built. The goal is to move from watching the experts to being the ones in charge as quickly as possible.

  4. Support. Ongoing help covers fixes, changes when a source system updates, and new reports as the business grows.

The internal owner does not need to be a data engineer. It is often an operations, finance, or IT leader who knows the business and can set priorities.

What should you have in place before deciding?

Whichever way you go, three things keep the first months on track.

  • One named owner. Someone who sets priorities and makes the call when two teams define a metric differently.

  • Five decisions you want data to support. Make them specific, like which locations to expand or which marketing channels bring customers back. These matter more than a long list of report requests.

  • Access to your systems. Arrange logins, exports, and approvals early. Waiting on access is one of the most common reasons data projects slow down.

Frequently asked questions

How much does it cost to hire a data engineer?

Robert Half's 2027 Salary Guide puts the US midpoint for data engineers at $160,500, with a range of $130,500 to $185,500. With benefits, the total cost at the midpoint is about $229,000 a year. Recruiting adds an average of $5,475 per hire before any agency fees.

Is it cheaper to outsource data engineering than to hire?

For most companies building their first data foundation, yes. A hire is a fixed cost of about $229,000 a year from day one. Outside help is spent in phases, so most of the cost falls during the build and drops to ongoing support afterward. Hiring becomes the better value once the foundation exists and there is steady, full time work to maintain and extend it.

Can a data analyst build a data warehouse?

Usually not alone. Analysts are trained to work with data that already sits in one place. Building a warehouse takes data engineering, which means pulling data from many systems, cleaning it, and keeping it current. Many companies hire an analyst first and find the foundation work stalls.

Do we need a data team before we can use AI?

You need clean, connected data before AI tools give reliable answers, but you don't need a full team. AI tools that sit on scattered or inconsistent data return wrong or incomplete answers. A solid data foundation, kept up by a small team or an outside partner, is enough for most AI uses.

Is it safe to give an outside team access to our data?

It can be, with the right controls. Expect role based access, a signed confidentiality agreement, and a clear record of who can see sensitive fields. For patient, payroll, or financial data, security should be designed in from the first week, not added later.

How long does it take to build a data foundation?

For a mid sized company with a handful of core systems, the first useful version often takes six to twelve weeks. The timeline depends mostly on how many systems are involved and how quickly access is granted.

The bottom line

Hire when the work is steady and the foundation already exists. Outsource to build the foundation and cover skills one person can't. For most growing companies, a mix of the two gets a working foundation in place in six to twelve weeks and keeps the knowledge inside the business.

Data Driven has completed more than 150 data projects since 2022, building data foundations and handing them off to internal teams. See how our managed data services work, or talk with us about a free one week blueprint for your company.

About the author

Maciek Brzakala is the CEO of Data Driven, a data and AI consultancy that works with companies across the US. Data Driven helps operating companies build reliable data foundations and put AI to work on real business problems.

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