Forward Deployed Data Engineer IV
New
E
E SourceUtility industry
Remote-based within the USFull-TimeSenior
Salary$175,000 – $200,000 USD + annual bonus
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Job Details
- Experience
- Five or more years of experience in data engineering, data platform, or analytics engineering
- Required Skills
- AWSDockerPythonSQLGitData engineeringSparkCI/CDDatabricks
Requirements
- 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.
- Experience building cloud-based data pipelines in AWS.
- Sound handling of incremental vs. full rebuilds, idempotency, late-arriving data, deduplication, and schema evolution.
- Proficiency with Git, Docker, CI/CD tools, and at least one modern orchestration framework.
- Demonstrated experience using AI coding assistants and agentic development tools.
- Strong written communication and whiteboarding skills.
- Working familiarity with utility or asset-heavy sector operational constraints.
- Ability to manage technical delivery scope, timelines, and measurable outcomes.
- Ability to navigate conflicting stakeholders from technical contributors to executives.
Responsibilities
- Embed with utility clients to design and build production data platforms end-to-end: ingestion, transformation, orchestration, quality, governance, and serving.
- Own the technical architecture of each engagement and defend it to client architects, security teams, and platform owners.
- Run technical discovery independently to translate business problems into technical solutions.
- Deliver a working, useful system early in each engagement and iterate toward production hardening.
- Lead migrations from legacy warehouses and on-premises systems to modern platforms.
- Model data using dimensional, lakehouse, medallion, and graph or semantic models.
- Integrate with utility source systems through APIs, change data capture, and other available methods.
- Make pipelines observable and operable with lineage, quality checks, alerting, and runbooks.
- Build and deploy operator-facing application layers and dashboards to ensure platform usability.
- Contribute reusable frameworks and reference architectures that scale across clients.
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