Forward Deployed Data Engineer IV, Databricks

At E Source, we help utilities make sense of complexity in a rapidly changing landscape, and we’re looking for a Forward Deployed Data Engineer IV to help shape how that impact shows up in the world.

E Source is a research, data/analytics, and technology focused professional services firm focused exclusively on the Utility industry in North America. We help utilities target and serve their customers more effectively, enhance and optimize their grid, and leverage operating best practices and technologies to manage their business more effectively. Headquartered in Texas, we have 450+ employees across the US and Canada. Learn more at www.esource.com http://www.esource.com

As a Forward Deployed Data Engineer IV, you’ll join our AI and Data Engineering (AIDE) team and embed directly with utility clients to understand their hardest operational problems, then build and ship the systems that solve them: production code, in the client’s environment, used by their people, and measured against their outcomes. This role focuses on getting a utility’s data into a state where decisions, applications, and models can actually be built on it: governed, modeled, reliable, and owned by the client’s team after the engagement ends.

You’ll own engagements end to end, from technical discovery through architecture, build, deployment, and handover, with no handoff points in between. Engagements move quickly: the expectation is trusted data flowing in the client’s environment within weeks, then hardened toward production based on how their teams actually use it. You’ll work closely with client data and IT teams and executives, and with E Source machine learning engineers, software engineers, data scientists, and consultants to deliver data platforms and data products for clients and to bring field learnings back into our products and practices.

In this role, you will:

• Embed with utility clients to design and build production data platforms end to end: ingestion, transformation, orchestration, quality, governance, and serving, using Databricks, Spark, Python, SQL, and Amazon Web Services (AWS)

• Own the technical architecture of each engagement and defend it to client architects, security teams, and platform owners

• Run technical discovery independently, separating the problem as stated from the underlying need, and say so when the requested solution is the wrong one

• Deliver a working, useful system early in each engagement, typically within the first few weeks, then iterate toward production hardening. Tactical solutions are legitimate; undocumented or unowned ones are not

• Lead migrations from legacy warehouses and on-premises systems to modern platforms, including undocumented dependencies

• Model data so that downstream teams can use it without re-deriving the same logic, using dimensional, lakehouse and medallion, and graph or semantic models where the domain calls for it

• Integrate with utility source systems such as asset and work management, historians and supervisory control and data acquisition (SCADA), customer information systems (CIS), advanced metering infrastructure (AMI), geographic information systems (GIS), and enterprise resource planning (ERP) through application programming interfaces (APIs), change data capture, file drops, and whatever is actually available

• Make pipelines observable and operable: lineage, quality checks, alerting, and runbooks the client’s engineers can act on

• Build and deploy operator-facing application layers, such as data quality and reconciliation dashboards, review and correction interfaces, and self-service data tools, so the platform is usable by client business teams and not just by their engineers. Databricks Apps is the preferred approach where the client runs Databricks

• Manage scope, timeline, and expectations against a defined statement of work, raising risk early

• Contribute reusable frameworks, ingestion patterns, and reference architectures that scale across c