Databricks Lead

At Staffworxs, we don’t just connect talent — we power transformation. Headquartered in Frisco, TX, with teams in Bengaluru and Hyderabad, we combine global reach with deep expertise. Our Digital & Data Analytics practice drives growth and innovation for some of the world’s top brands, who continue to retain us as their trusted partner. If you’re ready to make an impact, you’re in the right place. Title: Tech Lead – Data Engineer (Databricks) Work model: Onsite / hybrid; local candidates strongly preferred. Non-local candidates must be willing to work onsite in Cincinnati. Duration: Long term Contact Responsibilities: Role overview We are seeking a hands‑on, highly capable Lead Data Engineer / Databricks Tech Lead to lead the design, build, modernization, and ongoing evolution of customer‑data platforms supporting a large‑scale retail and digital ecosystem. This person will be the onsite technical lead, work independently with business and technology stakeholders, and provide technical direction to both onsite & offshore engineering teams. The ideal candidate is fundamentally strong in data engineering and distributed data systems, has deep Databricks expertise , communicates clearly with both technical and non‑technical partners, and can turn ambiguous business needs into scalable, production‑ready data solutions. Must Haves for this role: Strong hands‑on PySpark and SQL capability. Ability to independently design, troubleshoot, and deliver data‑platform solutions. Proven technical‑lead experience - not only project coordination. Strong verbal communication and stakeholder‑facing maturity. Experience guiding a distributed engineering team. Availability to work onsite in Cincinnati, Ohio. Key responsibilities Lead the technical design, architecture, and implementation of large‑scale data engineering solutions on Databricks. Design and build scalable, reliable, and maintainable data pipelines, data models, workflows, and data products for customer, loyalty, coupon, retail, and digital‑platform data. Establish and evolve a modern Databricks Lakehouse architecture, including data ingestion, transformation, orchestration, quality validation, serving layers, and operational monitoring. Develop production‑grade data pipelines using PySpark, SQL, Delta Lake, and Databricks‑native capabilities. Lead modernization initiatives for customer‑data and analytics platforms, including assessment, target‑state architecture, migration planning, implementation, validation, and production rollout. Define and enforce data engineering standards for coding, testing, CI/CD, code review, documentation, observability, performance optimization, and cost management. Drive best practices for data quality, schema evolution, data lineage, metadata, governance, access control, and privacy‑sensitive customer data. Work closely with application engineering, product, analytics, business, architecture, security, and infrastructure teams to gather requirements and align delivery plans. Translate business and customer‑data use cases into logical and physical data models, scalable pipelines, and reusable platform components. Act as the onsite technical owner and point of escalation for the offshore Data Engineering team; provide clear work breakdown, technical guidance, code reviews, prioritization, and mentoring. Prepare effort estimates, technical designs, implementation plans, risks, dependencies, and rollout approaches for data engineering initiatives. Troubleshoot complex data, performance, pipeline, reliability, and production‑support issues. Communicate progress, risks, architecture decisions, and delivery status effectively to stakeholders and leadership. Champion continuous improvement across engineering practices, operational maturity, delivery velocity, and platform reliability. Required qualifications 10-15+ years of hands‑on experience in Data Engineering, software engineering, data platforms, or a closely related technical discipline. 3+ years of strong, production‑level experience with Databricks. Deep hands‑on expertise with PySpark, Spark SQL, Python, SQL, distributed processing, and performance tuning. Strong experience designing and implementing end‑to‑end data pipelines, including batch and, ideally, streaming data workloads. Practical experience with Delta Lake, Delta tables, partitioning, file optimization, schema evolution, data quality checks, and data lifecycle management. Experience designing data lakehouse, data warehouse, or enterprise customer‑data platform architectures. Strong understanding of data modeling, including dimensional modeling, curated data products, and customer/entity‑level data design. Experience with cloud‑based data engineering, preferably Azure and Azure Databricks. Experience with CI/CD, source control, code review, automated testing, deployment automation, and environment promotion for data pipelines. Strong ability to independently assess ambiguous requirements, make sound technical decisions, and drive complex work to completion. Demonstrated experience leading engineers technically, mentoring team members, and coordinating distributed or offshore teams. Excellent verbal and written communication skills, including the ability to explain technical concepts, architecture tradeoffs, and delivery risks to diverse stakeholders. Preferred qualifications Experience supporting customer, loyalty, coupon, retail, eCommerce, payment, customer‑experience, or marketing data domains. Experience building customer 360, identity‑resolution, segmentation, personalization, offer, promotion, or analytics data products. Experience with Databricks Workflows, Unity Catalog, Delta Live Tables, Auto Loader, Structured Streaming, and/or Databricks Asset Bundles. Experience with Azure Data Factory, Azure DevOps, ADLS Gen2, Event Hubs, Kafka, or similar cloud data services. Familiarity with data governance, PII handling, data masking, role‑based access control, auditability, and enterprise data compliance requirements. Experience leading modernization programs involving legacy data warehouses, ETL platforms, or fragmented customer‑data environments. Background in large enterprise retail, digital commerce, consumer‑data, or high‑volume transactional environments. Staffworxs is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive workplace for all employees, regardless of race, color, religion, gender, sexual orientation, national origin, age, disability, or veteran status. #J-18808-Ljbffr