Data Engineer
Tekwissen
Overview: TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Our client provider of digital technology and transformation, information technology and services
Position: Data Engineer Location: Frisco, Texas or Virginia
Duration: 6 Months Job Type: Temporary Assignment Work Type: Onsite JOB SUMMARY:
Position: Data Engineer Location: Frisco, Texas or Virginia
Duration: 6 Months Job Type: Temporary Assignment Work Type: Onsite JOB SUMMARY:
- P&SC Data and Intelligence
- Microsoft fabric, Databricks, Data pipelines, Data products, Kafka, Azure event hub
- Apache airflow, Azure data factory, Medallion architecture, ci/cd, devops, tms
- The Data Engineer (Level IV) designs, builds, and operates the data pipelines and lakehouse data products that power P&SC intelligence across device supply chain, reverse logistics, procurement, network supply chain, and real-time control tower capabilities.
- Operating within a squad focused on a specific SC domain or cross-cutting platform function, the Data Engineer is a hands-on technical contributor who builds to production quality, owns pipeline reliability and KTLO, and actively raises the engineering standard of the team around them.
- Design and build production-grade data pipelines spanning source ingestion, bronze landing, silver transformation, and gold-layer data product delivery within the squad's domain scope.
- Own pipeline KTLO (Keep the Lights On) for assigned data products, including monitoring, alerting, incident response, and ongoing reliability improvements.
- Implement data ingestion patterns for assigned source systems including batch file ingestion, API-based ingestion, and event-driven streaming (Kafka, Azure Event Hub) depending on squad scope.
- Apply medallion architecture (bronze, silver, gold) and Fabric IQ certification standards consistently across all data product builds.
- Collaborate with System Analysts to implement field-level transformations, business rule logic, and data quality checks as specified in product requirements documentation.
- Participate in and contribute to pipeline design reviews, ensuring solutions align with the organization's Databricks and Fabric engineering standards.
- Support the migration and deprecation of legacy platforms including SCOpsBI SQL Server and SAP boundary systems, following the organization's extract, validate, rebuild, cutover, and decommission pattern.
- Write and maintain comprehensive pipeline documentation including data lineage, transformation logic, SLA definitions, and dependency maps.
- Contribute to the organization's DevOps and engineering reliability practices including CI/CD pipeline setup, testing frameworks, and incident runbooks.
- Mentor and technically guide junior and mid-level data engineers within the squad.
- 7-10 years of data engineering experience with a strong track record delivering production data pipelines in large enterprise environments.
- Expert proficiency in PySpark or Spark SQL for large-scale data transformation on distributed compute platforms.
- Hands-on experience with Databricks including Delta Lake, Unity Catalog, and workflow orchestration.
- Experience with Microsoft Fabric or Azure Synapse Analytics; familiarity with Fabric IQ and OneLake is a plus.
- Proficiency with pipeline orchestration tools such as Azure Data Factory, Databricks Workflows, or Apache Airflow.
- Solid SQL skills for data validation, transformation logic, and ad-hoc source system analysis.
- Experience building and maintaining ingestion pipelines from enterprise operational systems such as ERP, WMS, TMS, or comparable platforms.
- Strong understanding of data quality frameworks, including implementing checks, alerting on anomalies, and maintaining SLA-compliant pipeline health.
- Experience with DevOps practices for data pipelines including version control (Git), CI/CD, and automated testing.
- Ability to operate independently on complex technical problems with minimal oversight while maintaining clear technical documentation.
- Experience with real-time streaming technologies including Kafka, Azure Event Hub, Delta Live Tables, or Spark Structured Streaming.
- Familiarity with Oracle, ERP ingestion patterns, or large-scale ERP migration programs. Background in supply chain, logistics, reverse logistics, or procurement data domains.
- Experience with legacy platform migration and decommission programs. Exposure to T-Mobile's Omni lakehouse platform or MagentaBuilt integrations.
- Python development skills beyond PySpark, including utility scripting and framework contributions.
Vacancy posted 3 days ago
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