Data Engineer (Databricks)

Data Engineer (Databricks) at eNGINE Data Engineer (Databricks) at eNGINE in Braddock, Pennsylvania Posted in about 1 hour ago.

Type:

full-time

Job Description:

eNGINE builds Technical Teams. We are a Solutions and Placement firm shaped by decades of interaction with Technical professionals. Our inspiration is continuous learning and engagement with the markets we serve, the talent we represent, and the teams we build. Our Consulting Workforce is encouraged to enjoy career fulfillment in the form of challenging projects, schedule flexibility, and paid training/certifications. Successful outcomes start and finish with eNGINE . eNGINE is seeking a Senior Data Engineer with extensive hands-on expertise in Databricks and modern enterprise data platforms, ideally within large, complex organizational environments. This role is suited for someone who has successfully designed and delivered scalable data solutions-preferably in greenfield implementations-and is comfortable partnering directly with Fortune 500 clients. You will be relied upon to drive technical strategy, make thoughtful architectural decisions, and help develop the skills and capabilities of fellow engineers. You will collaborate closely with both clients and internal teams to architect and build enterprise-grade data platforms and pipelines from the ground up. The ideal candidate can operate confidently across the full solution lifecycle-from early architecture and design through implementation and optimization-while ensuring solutions remain practical, dependable, and aligned with business objectives.

Responsibilities:

Design and implement large-scale Databricks-based data platforms (Delta Lake, Spark, Unity Catalog) on Azure Create and support batch and streaming data pipelines handling high-volume, complex datasets Establish medallion/lakehouse architectures from scratch in greenfield environments Develop and optimize data models to enable analytics, reporting, and downstream use cases Integrate Databricks with broader enterprise systems (APIs, event streams, data warehouses, ML workflows) Optimize Spark workloads for performance, stability, and cost efficiency at scale Support production environments, including CI/CD workflows, testing strategies, and release processes Engage directly with enterprise clients to turn business requirements into effective technical solutions Partner with architects, engineers, and data scientists across multiple initiatives Maintain a balance between rapid delivery and solution robustness, adjusting as needed Make informed, pragmatic decisions in evolving and ambiguous environments, particularly during greenfield builds Remain hands-on while also shaping design patterns and approaches across the team Clearly communicate technical tradeoffs to both technical and non-technical audiences Operate within modern engineering best practices (version control, code reviews, automated testing) Demonstrate a consistent ability to coach, mentor, and guide data engineers and analysts End-to-end delivery of Databricks-driven data solutions, from initial design to production support Technical leadership and architectural decision-making for large-scale implementations Reliability, monitoring, and incident management of data pipelines in production Performance optimization and cost management of Databricks and Azure workloads Data quality standards, governance alignment, and compliance with enterprise security requirements Development of reusable frameworks, patterns, and standards to support future scalability Mentorship and ongoing technical growth of engineers across the team

Required:

Bachelors Degree Significant experience building Databricks solutions from the ground up Proven ability to communicate effectively in client-facing environments