$185,000–$300,000
From implementation and data engineering to FDE
765 of 2,493 postings ask for implementation and data engineering · 369 companies · 569 with pay · median $190,000 · middle half $160,000–$216,000 · read
- You already have the delivery experience FDE teams need, so the work is making it easy to evaluate.
- "Implemented a data platform" hides the answer — name whether you configured a product, wrote production code, or ran a migration.
- The exception you handled instead of forcing a customer onto the standard path is the strongest thing you can show.
- Read an AI requirement at the level of the work, because integrating a model and maintaining an AI platform are different jobs.
- Say what you have not shipped and ask whether onboarding covers it, rather than turning a prototype into production AI experience.
Forward Deployed Engineer IV, Data Cloud, Google Cloud
San Francisco, CA · Austin, TX · +5 moreSan Francisco, CA +6
$207,000–$300,000
Sr. Forward Deployed Engineer - Public Sector
Berkeley, CA · Kansas City, MO · +1 moreBerkeley, CA +2Remote
$182,000–$250,208
$136,948–$167,380
$185,000–$300,000
$192,000–$455,000
Forward Deployed Engineer III, Data Cloud, Google Cloud
San Francisco, CA · Austin, TX · +5 moreSan Francisco, CA +6
$174,000–$252,000
$150,000–$275,000
The experience to bring forward
Choose examples that carry both a technical boundary and a user boundary:
- a pipeline, integration or software workflow that reached regular use
- a user request you turned into a technical plan and a delivery sequence
- APIs, permissions, data-quality checks, testing, monitoring or incident resolution you owned
- a migration where you handled an exception instead of forcing the customer onto a standard path
- enablement or documentation that helped the customer operate the delivered system
Name the systems and the decision you made. State whether the work was a prototype, a proof of concept, a production deployment or ongoing operations.
A Veeam forward deployed engineer posting shows the range: customer deployments across cloud, hybrid and on-premises environments, integrations, data pipelines, automations, agent-based workflows, and security and governance requirements turned into technical plans.
Handling AI requirements
| What the posting asks for | What to prepare |
|---|---|
| Integrate a model into a production workflow | The data path, access controls, failure handling, and how the output is reviewed |
| Evaluate output against a benchmark | An evaluation set, the failure analysis, and what you changed as a result |
| Maintain an AI platform | Observability, cost and latency limits, rollback, and incident work |
| Build a new application | End-to-end design, from the user's task through monitoring |
If you have not shipped an AI system, describe the adjacent evidence plainly: data contracts, evaluation design, observability, access controls, rollback plans and production incident work. Then name the missing piece and ask whether the team supports it during onboarding.
How to write the transition story
Use one project to connect four points: the user's need, the systems you changed, the deployment or handoff, and the responsibility that remained. Also say what you did when the standard implementation failed. A concise account of the constraint, the options and the trade-off tells the team more than a list of products.
Five questions for the team
- Is the role mainly integrations, application development, configuration or model evaluation?
- Does the engineer own production incidents after the customer goes live?
- Which cloud, data and deployment environments are common in the customer base?
- How much of the role is writing code against configuring a platform or running enablement?
- What AI experience is required at hiring, and which gaps can onboarding support?
Stated in the posting
585 of these 765 postings state a minimum number of years, most often 3–5 years.
Share of those 585.
10.4%50.6%33.2%
- 0–2 years61 postings · 10.4%
- 3–5 years296 postings · 50.6%
- 6–9 years194 postings · 33.2%
- 10+ years34 postings · 5.8%
180 postings state no number.
Our reading of the posting
424 of these 765 postings build what the customer runs.
55.4%17.5%11.4%11.0%
- Builds what the customer runs424 postings · 55.4%
- Integrates and configures it134 postings · 17.5%
- Plans what the customer deploys first87 postings · 11.4%
- Builds for teams inside the company84 postings · 11.0%
- Leads the deployment team29 postings · 3.8%
- Proves it works before the sale5 postings · 0.7%
- Keeps customers running after launch2 postings · 0.3%
Frequently asked questions
What do FDE and deployment jobs asking for implementation and data engineering experience pay?
569 of 765 postings state a pay range. The median is $190,000, and half of them sit between $160,000 and $216,000, from 250 companies.
Which companies are hiring for FDE and deployment jobs asking for implementation and data engineering experience?
These 765 postings come from 369 companies. The employers with the most postings are Databricks (67), Deloitte US (61), Amazon (incl AWS) (29), OpenAI (18) and Accenture (15).
Are FDE and deployment jobs asking for implementation and data engineering experience remote?
149 postings allow remote work from the US. Another 199 are hybrid and 120 are on-site. The remaining 297 say nothing about arrangement. Travel is mentioned in 392 of the 765 postings.
Employer postings · Counted over the postings that state what experience they want. A posting that states none, or that asks for experience outside the five we list, is in none of them. · How we read postings