Data Engineering Lead

Oxford Global Resources•Pennsylvania Remote, PA

4d•$120,000 - $150,000•Remote

About The Position

Oxford Global Resources is seeking a hands-on Data Engineering Lead to guide a team of two to four engineers and shape the next stage of the company’s enterprise data platform. Reporting to the Director of Data Operations, this role balances team leadership with direct involvement in architecture, development, troubleshooting, and delivery. The data environment spans Azure and Microsoft Fabric. The lead will connect these ecosystems, reduce redundancy, and develop a practical roadmap toward a consolidated Fabric platform while maintaining business continuity. The role establishes engineering standards, strengthens platform reliability, and partners with analytics, technology, and business stakeholders to deliver dependable data products. The position is remote in the Eastern United States and exempt. The annual base salary range is $120,000–$150,000, with possible eligibility for an annual performance-based bonus of 10% of base salary.

Requirements

Five or more years of data engineering experience, including at least two years formally leading engineers or serving as a technical lead.
Strong hands-on experience using Microsoft Fabric in a production environment, beyond evaluation or proof-of-concept work.
Working knowledge of Azure data services, including Azure Data Factory, Synapse, ADLS Gen2, and Azure SQL, and their integration or overlap with Microsoft Fabric.
Advanced T-SQL skills, including writing, tuning, testing, and maintaining stored procedure-based ETL at scale.
Experience orchestrating and monitoring transformation pipelines and diagnosing and resolving production failures.
Proficiency in Python and working knowledge of Spark or PySpark.
Foundation in dimensional and lakehouse or medallion data modeling, and modern ETL and ELT design patterns.
Experience with CI/CD and infrastructure as code, using tools such as Azure DevOps or GitHub Actions and Terraform or Bicep.
Clear communication, sound judgment, and ability to influence stakeholders while balancing hands-on delivery with team leadership.

Nice To Haves

Microsoft Certified Fabric Analytics Engineer Associate DP-600 or Fabric Data Engineer Associate DP-700 certification.
Experience managing data from enterprise source platforms such as Salesforce, ATS or VMS platforms, and ERP or finance systems, ideally in staffing or business services.
Experience leading a data-platform migration or consolidation.
Familiarity with applying AI to the data estate, including grounding large language models and natural-language query tools in governed semantic models and evaluating the accuracy of AI-generated answers.

Responsibilities

Lead, mentor, and develop a team of data engineers through prioritization, project planning, code reviews, coaching, and career development.
Own platform strategy across Azure and Microsoft Fabric; identify overlapping pipelines, storage, and tooling and create a roadmap to consolidate or retire redundant components.
Build and mature the Microsoft Fabric platform, including Lakehouse, Warehouse, Data Factory pipelines, and OneLake, and determine which capabilities should remain in Azure or move to Fabric.
Develop complex T-SQL queries, stored procedures, and database solutions; manage, monitor, and optimize SQL Server environments for performance, reliability, and scalability.
Design, optimize, test, and maintain stored procedures supporting core ETL processes, using disciplined version control and performance management.
Manage transformation pipelines end to end, including orchestration, scheduling, monitoring, failure handling, troubleshooting, and recovery.
Lead impact analysis and change management for major upstream source-system changes, coordinating with platform owners and downstream stakeholders.
Establish and reinforce standards for data quality, testing, observability, documentation, security, and CI/CD.
Partner with analysts, BI developers, technical teams, and business stakeholders to translate requirements into reliable, scalable data products.
Manage platform reliability, performance, and cloud cost while applying governance practices for access, lineage, compliance, and PII handling.
Evaluate emerging tools and approaches, make build-versus-buy recommendations, and communicate priorities, risks, tradeoffs, and progress to technical and nontechnical leaders.