Senior Data Engineer - Ingestion & Provenance Architect

Dell Technologies Inc.

Round Rock (TX)

On-site

USD 146,000 - 243,000

Full time

14 hours ago
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Job summary

Dell Technologies Inc. is seeking a senior data engineer to build and operate the ingestion pipelines that bring external data into the Knowledge & Intelligence layer.

You will own contracts with upstream teams, develop connectors, ensure freshness SLAs, and maintain a reliable landing zone.

This greenfield role emphasizes high-volume data, cost-aware partitioning, and a provenance-driven approach, with strong Python as the core tool.

Qualifications

  • Strong Python — your primary language for building and operating pipelines, connectors, and tooling.
  • Hands-on data sourcing and ingestion in complex environments.
  • Proven track record landing upstream sources into a data lake reliably.
  • Experience handling high-volume data with cost, partitioning and performance focus.
  • Provenance mindset — every record carries its origin.
  • Strong collaboration with upstream domain teams.

Responsibilities

  • Negotiate schema, freshness SLAs, and change notices with upstream domain teams.
  • Build and operate connectors and change-data-capture that land upstream sources into the lake.
  • Own the raw landing layer — structure, partitioning, and a clean handoff to the medallion pipeline.
  • Know when a source breaks, drifts, or goes stale — before anything downstream does.
  • Nothing lands ungrounded; provenance travels with every record from the moment it arrives.

Skills

Python
Data engineering
Data ingestion
Stakeholder collaboration

Tools

ETL tooling

Job description

Dell Technologies Inc. is seeking a senior data engineer to build and operate the ingestion pipelines that bring external data into the Knowledge & Intelligence layer.

You will own contracts with upstream teams, develop connectors, ensure freshness SLAs, and maintain a reliable landing zone.

This greenfield role emphasizes high-volume data, cost-aware partitioning, and a provenance-driven approach, with strong Python as the core tool.

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