Forward-Deployed Data Engineer

Cognizant; Plano (TX); On-site; USD 110,000 - 150,000; Full time

Job summary

Cognizant in Plano, TX seeks a Forward-Deployed Data Engineer to build and operate production-grade data pipelines within a fast-moving super-agent workstream. You will turn designs into reliable pipelines, ensure data quality and lineage, and support a range of autonomous data tooling.

A strong background in Spark, SQL, Python, and cloud platforms is required. Responsibilities include deploying ETL/ELT pipelines, validating data quality, and maintaining Run Books and knowledge artifacts for

Qualifications

• 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 in a real delivery setting.
• Comfortable owning a ticket from L1 triage through to root-cause fix.

Responsibilities

• Build and deploy ETL/ELT pipelines using PySpark and code-generation tooling; create Bronze→Silver→Gold transforms.
• Implement and tune data quality rules and lineage; validate column-level lineage.
• Operate and tune Ignition super-agents (Data Discovery, Virtual Data Engineer, etc.).
• Create and run test-case suites; support UAT cycles.
• Serve as escalation point for L1/L2 support and document resolutions for the repository.

Skills

Databricks / Spark

SQL

Python

GenAI tooling

Cloud platforms

Tools

Delta Lake

Delta Live Tables

Unity Catalog

Job description

Job Title- Forward-Deployed Data Engineer

Mission

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.

Key Responsibilities

• 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.

Required Experience & Qualifications

• 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.

Technical & Platform Fluency

• 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.