Job Description
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Job Title- Forward-Deployed Data Engineer
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Location0 Hybrid @ Plano, TX
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Mission
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The SME is the hands-on builder and operator embedded in a specific super-agent workstream or sector pod — the person who turns a Staff Architect's design into a working, production-grade pipeline, model, or agent, and who is frequently the first responder when an incident hits L1/L2.
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Key Responsibilities
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- Build and deploy. Implement ETL/ELT pipelines using PySpark and zero-error code-generation tooling (Codegen, AI Dev Kit); build Bronze→Silver→Gold transforms and STTM-driven mappings for assigned Data Products.
- Data quality and lineage execution. Implement and tune DQ rules (ICEDQ, DLT Expectations); build and validate column-level lineage in Unity Catalog; keep the EDF DQ scorecard current.
- Super-agent operation. Operate and tune one or more Ignition super-agents day to day — for example the Data Discovery agent, Virtual Data Engineer, Virtual Data Tester (DVS), or Self-Service Agent — the literal hands on the keyboard for agentic tooling.
- Testing and validation. Build and run test-case suites via the Data Validation Suite; execute parallel-Silver validation ahead of controlled cutovers; support UAT cycles.
- Sustain and support. Serve as the escalation point for L1/L2 conversational support; use autonomous-RCA output to accelerate troubleshooting; feed every resolved scenario back into the deterministic repository so the platform keeps getting smarter.
- Documentation and knowledge transfer. Maintain Application Run Books, DRD/DAT profiling records, and the unified knowledge-fabric catalog entries for assigned Data Products.
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Required Experience & Qualifications
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- 4–8 years of hands-on data engineering experience.
- Working proficiency with Databricks/Spark, SQL, and at least one major cloud platform (Azure preferred).
- Direct exposure to GenAI-assisted development tooling (code generation, conversational data assistants) in a real delivery setting.
- Comfortable owning a ticket from L1 triage through to root-cause fix, not just writing the original pipeline.
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Technical & Platform Fluency
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- PySpark, SQL, Python; Delta Lake and Delta Live Tables; Unity Catalog fundamentals.
- Git-based CI/CD for data pipelines.
- ICEDQ or an equivalent data-quality/reconciliation tool.
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