What is data engineering, and when does a mid-size company need it?
Data engineering is the work of moving, connecting, cleaning and storing data so that reports and AI can rely on it: the pipelines, integration, storage and data quality underneath everything else. Data science analyzes that data; data engineering makes it usable first. If your systems don't talk to each other, you have a data engineering problem, not a reporting problem.
What is data engineering?
Data engineering is plumbing, and we mean that as a compliment. Data engineers build and run the pipelines that pull data out of the systems where work happens, the integrations that keep those systems in step, the storage where combined data lives, and the checks that catch bad data before it reaches a dashboard.
In a mid-size manufacturer or automotive supplier, that usually means an ERP, a CRM, a quality system, supplier portals and a shelf of spreadsheets, each holding part of the truth about the same order. Data engineering is what turns those into one version that people and software can trust.
On first calls, we often hear some version of "our systems don't talk to each other." That sentence is a data engineering brief. It just hasn't been written down yet.
Is data migration the same as data engineering?
Data migration is one job within data engineering: moving data from an old system to a new one, once, and getting it right. It uses the same skills (mapping fields, cleaning records, checking that nothing went missing) but it has an end date.
The mistake is treating a migration as the whole project. If the new system still doesn't talk to the others once the move is done, you've relocated the problem rather than fixing it. A good migration plan includes the integrations that keep the data clean afterward.
Data engineering vs data science: what's the difference?
The two get confused because they sit next to each other on org charts. The split is straightforward:
- Data engineers build and run the pipelines, integration, storage and data quality that feed analytics.
- Data scientists analyze and model that data to answer questions and make predictions.
Data science gets the attention. The U.S. Bureau of Labor Statistics projects 35% growth in data scientist jobs from 2025 to 2035, with a median pay of $120,230. The BLS has no separate category for data engineers, which says something about how invisible the plumbing is.
The practical point: hiring a data scientist before the data engineering is done tends to produce an expensive person who spends most of the week cleaning spreadsheets. Order matters.
Why do systems that don't talk to each other cost so much?
Because every gap between systems gets filled by a person. Someone rekeys the order, reconciles the report, or exports the file every night and hopes nothing broke.
The scale is bigger than most people expect. MuleSoft's 2025 Connectivity Benchmark found the average enterprise running 897 applications, with only 29% of them integrated.
Poor data has a price too. Gartner research from 2020, still its headline figure, estimates that poor data quality costs organizations at least $12.9 million a year on average. A mid-size company's figure will be smaller, but the causes are the same: duplicate records, conflicting definitions and decisions made on stale numbers.
In manufacturing and the automotive supply chain, the cost shows up as a planner who can't see supplier status, or a plant that learns about a quality hold after parts have shipped. One example from our partner bench: Decypher Corp built a custom ERP for a global automotive OEM that consolidated fragmented supply-chain data into one platform and standardized the workflows around it. You can read the summary on our results page.
Another Decypher project shows the same pattern in a smaller company: a construction contractor whose job-site reports were re-typed into payroll and invoicing. Connecting those systems, so time and job data flow through once, saved more than 35 hours of work a week across the business.
ETL or ELT: which should you use?
Most data engineering pipelines follow one of two patterns. The difference is where the cleanup happens.
| ETL (extract, transform, load) | ELT (extract, load, transform) | |
|---|---|---|
| Where data is transformed | On a separate server, before loading | Inside the warehouse, after loading |
| Best suited to | Structured data | Structured and unstructured data |
| Speed | Slower, because transformation comes first | Faster to load, since raw data goes in first |
That summary follows AWS's and IBM's own comparisons. In practice many companies run both: ETL for tidy financial and ERP data, ELT for unstructured sources.
Then there's integration between live systems, which is a slightly different job from loading a warehouse. That's where platforms like MuleSoft come in: Salesforce to SAP, to the warehouse, to whatever else holds a version of the truth, with one managed pipeline instead of nightly exports. Salesforce made its direction clear when it completed its acquisition of Informatica on 18 November 2025, for about $8 billion, to strengthen Data 360 and MuleSoft integration.
Do you need data engineering before AI?
Yes, and the numbers are blunt about it. Gartner predicted in February 2025 that through 2026, organizations will abandon 60% of AI projects that lack AI-ready data. An agent or model reading duplicate, conflicting records will produce confident, wrong answers faster than a person could.
That's why our Salesforce work runs in a fixed order: connect first, clean second, automate third. Done that way, each step pays for the next.
Where should a mid-size company start with data engineering?
- Name the decision that keeps going wrong. "We can't see on-time delivery by supplier" is a better starting point than "we need a data strategy."
- List the systems that decision depends on, and which one is the source of truth for each field.
- Find the manual steps between them: the exports, rekeying and reconciliations.
- Fix one flow end to end, measure the hours or errors it removes, then do the next.
Where Salesforce is at the center, MSquare Technology handles MuleSoft integration and data engineering; see our Salesforce and AI page. Where the answer is a purpose-built system or reporting layer, Decypher does that work, described on our custom software page. Either way, start with the decision, not the tool.
Connecting Salesforce and Databricks? Our free Salesforce to Databricks route finder shows which connection fits your data.
Sources
- Decypher Corp, "Streamlining the Job Site Reporting Process" case study (decyphercorp.com, accessed September 2026)
- U.S. Bureau of Labor Statistics, "Data Scientists," Occupational Outlook Handbook (August 2026)
- Salesforce, "Integration Key as 93% of IT Leaders Turn to AI Agents Amid Soaring Resource Demands – New Research" (2025)
- Gartner, "Data Quality: Why It Matters and How to Achieve It" (2020 research)
- AWS, "ETL vs ELT - Difference Between Data-Processing Approaches" (accessed September 2026)
- IBM, "ELT vs. ETL: Similarities and Differences" (accessed September 2026)
- Salesforce, "Salesforce Completes Acquisition of Informatica" (November 2025)
- Gartner, "Lack of AI-Ready Data Puts AI Projects at Risk" (February 2025)
