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

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 clients, and provide product and implementation feedback to E Source engineering and product teams

Apply data governance, security, privacy, and retention controls appropriate to the utility regulatory context

You’re likely a great fit if you:

Own the full lifecycle of a system, from discovery through architecture, build, deployment, and post-go-live support, rather than a single phase of it

Ship on compressed timelines, going from first conversation to a working system in weeks rather than quarters, with the judgment to know what to defer and what cannot be

Work directly with enterprise clients and navigate conflicting stakeholders, from technical individual contributors to executives

Manage technical delivery scope, timelines, and measurable outcomes

Communicate clearly in writing and at the whiteboard, and are comfortable with ambiguity, incomplete data, and objectives that change mid-engagement

Exercise informed judgment about which approach a problem actually needs, rather than defaulting to a familiar one

And even better if you:

Hold a master’s degree in a relevant science, technology, engineering, or mathematics (STEM) field

Have graph database and knowledge graph modeling experience

Have run streaming platforms (Kafka or equivalent) and change data capture tooling in production

Have built feature pipelines and served data to machine learning (ML) and artificial intelligence (AI) systems

Have built and deployed Databricks Apps, or comparable operator-facing applications on a modern frontend framework (React, Streamlit, or Dash) with REST or GraphQL APIs

Have optimized cost and performance in Databricks: partitioning, file layout, clustering, caching, and compute right-sizing

Hold a Databricks certification

Experience and Skills to Qualify Include:

Bachelor’s degree in computer science, information technology, or a related field

Five or more years of experience in data engineering, data platform, or analytics engineering, with at least one platform owned in production

Expert proficiency in Python, SQL, Databricks, and Spark, including Spark runtime internals and the ability to diagnose why a job is slow rather than adding compute

Experience building cloud-based data pipelines in AWS; working knowledge of a second cloud ecosystem preferred

Sound handling of the recurring hard problems: incremental versus full rebuild, idempotency, late-arriving data, deduplication, backfill strategy, and schema evolution

Batch and streaming pipeline design, with informed judgment about which the problem actually needs

Knowledge of data governance, security, privacy, lineage, and quality practices

Proficiency with Git, Docker, continuous integration and continuous delivery (CI/CD) tools, and at least one modern orchestration framework

Demonstrated experience using AI coding assistants and agentic development tools to rapidly prototype, test, and iterate on working software, compressing the path from idea to running system. This is a top skill set for the role

Experience in the energy or utility industry, or another asset-heavy sector, with working familiarity of the systems, data, and operational constraints utilities live with

Strong written communication and whiteboarding skills

At E Source, you’ll work alongside people who are thoughtful, curious, and deeply knowledgeable about utility and energy systems. We value clarity over jargon, substance over flash, and collaboration over ego. You’ll have the opportunity to grow your skills, contribute to meaningful work, and help shape a company that plays a real role in how the energy future unfolds.

What you can expect:

Excellent insurance options, including medical, dental, and vision plans; company-paid life insurance; company-paid long- and short-term disability insurance; medical and dependent-care flexible spending plans, and paid parental leave.

A flexible time off (FTO) policy that provides paid time away from work, approved by your man