Job Description
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Company Overview
A technology-focused organization is seeking a Senior Data Engineer to build data infrastructure that supports AI-ready and autonomous systems. The role focuses on connecting fragmented enterprise data environments, developing reliable production pipelines, and transforming complex data landscapes into accessible, structured foundations for AI and automation.
Summary
The Senior Data Engineer will work at the intersection of data architecture, pipeline engineering, and AI readiness. This role involves assessing enterprise data environments, connecting disparate systems, building production-grade data infrastructure, and partnering with AI Solutions Engineers to ensure data can support advanced AI and autonomous workflows.
Responsibilities
- Lead data legibility and eligibility audits to assess whether client data is structured for AI applications and accessible to AI systems.
- Connect fragmented enterprise data environments, including ERP systems, CRM platforms, spreadsheets, email sources, and other business systems.
- Build unified data layers that enable autonomous workflows and AI agents to access and reason over business data.
- Design and deploy production data pipelines across cloud environments, including both batch and streaming architectures.
- Identify and resolve data trapped within static reports and PDF documents by tracing information back to its source systems.
- Build data infrastructure that supports autonomous workflows and AI-driven applications.
- Collaborate with AI Solutions Engineers to ensure data architecture supports agent-level reasoning rather than traditional dashboarding alone.
- Work directly with clients to understand complex data environments and develop practical integration and architecture solutions.
Qualifications
- 6+ years of experience in data engineering, with significant experience working across enterprise environments.
- Strong proficiency in SQL and Python.
- Experience with modern data orchestration frameworks such as Airflow, Dagster, or Prefect.
- Deep experience with cloud data platforms such as BigQuery, Snowflake, or Redshift.
- Experience with cloud infrastructure and services, particularly GCP or AWS.
- Hands-on experience integrating data from enterprise systems such as SAP, Salesforce, NetSuite, or legacy ERP platforms.
- Experience developing real-time data pipelines and event-driven architectures.
- Ability to explain complex data architecture decisions to senior executives in terms of business impact.
Preferred Qualifications
- Experience with data migration or integration projects involving M&A, ERP implementations, or digital transformation programs.
- Industry experience in manufacturing, logistics, healthcare, or professional services.
- Consulting experience and familiarity with client engagements involving scoping, delivery, and rapid iteration.
- Experience building production data infrastructure specifically for machine learning or AI workloads.
This Role May Not Be a Fit If
- Your experience has primarily involved clean, well-documented datasets within a single data warehouse.
- You primarily prefer developing internal analytics dashboards rather than production data systems.
- You are looking for a stable, long-term codebase instead of engagement-based work across different client environments.
- You are uncomfortable presenting technical findings and recommendations to non-technical executives.
Disclaimer
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