Data and integration FDE jobs

Data and integration FDE jobs make a product fit the systems around it. The work may involve APIs, data pipelines, identity, schemas, cloud and on-premises boundaries, workflow automation, and the people who use the result. The job title can vary widely; the responsibilities reveal whether the role is building a working connection or only discussing one.

What you may own

An integration starts with discovery. The AECOM Forward Deployed Engineer, Internal Data Products posting describes discovering workflows with construction teams and business leaders, then building internal applications and automations across interfaces, logic, databases, and APIs. It also connects those applications to governed enterprise and project data. This is FDE work because the engineer has to understand the operating workflow and make the technical system useful inside it.

Customer-facing roles may combine the same work with external delivery. The Veeam posting describes deploying across AWS, Azure, Google Cloud, private cloud, and on-premises systems; building integrations and data pipelines; and translating security, privacy, governance, and AI requirements into technical plans. A Google customer engineer posting calls out integrations across data pipelines, identity, connectors, and compliance boundaries, as well as diagnosing deployment blockers at code level.

The implementation details matter. A credible integration plan says which system owns each field, how authentication works, what happens when a request fails, how schemas change, and who receives an alert. It also says where the data may be stored and which customer or internal team operates the connection. “Integrate with the customer’s systems” is an opening for those questions, not a complete scope.

Skills to look for

Useful evidence includes:

• API design, authentication, webhooks, queues, retries, and rate limits;
• SQL or other data stores, data mapping, validation, and schema migration;
• cloud, containers, networking, and deployment across more than one environment;
• observability, runbooks, incident handling, and a clear handoff;
• requirements discovery with users who know the business process better than the engineer.

How this differs from data engineering alone

Data engineering can focus on internal pipelines, platform reliability, and recurring data processing. A data and integration FDE may do those things, but also has to discover the customer’s process, explain trade-offs, adapt to a local environment, and confirm that people can use and operate the result. Conversely, an FDE role may configure an existing connector rather than build a new data platform. Ask how much code is expected, which systems are in scope, and whether the role owns production support.

Before applying, ask who owns the source data, who approves access, whether customer environments are standardized, and what happens when a requested integration becomes a product change. Ask how the team separates reusable platform work from one-customer exceptions. Ask about travel and permitted work location as well; postings in the live list frequently leave those terms unstated.

See jobs mentioning integration, FDE research, and the path from implementation or data engineering. You can also compare the production AI job type when the integration supports an AI system in production.