Staff Data Engineer

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Staff Data Engineer (P4)

ROLE SUMMARY
The Staff Data Engineer sets the technical direction for the governed data foundations, pipelines, APIs and Databricks semantic layer that make enterprise data trustworthy, AI-ready and accessible to non-technical colleagues. This role owns architecture and standards across multiple teams and domains, remains hands-on in Databricks and PySpark for the most complex problems, and acts as a trusted technical authority to Finance, IT and business leadership in moving the organization to a modern, agile target state data architecture.

RESPONSIBILITIES

• Define and own the technical architecture and roadmap for information infrastructure and data management processes that move the organization to an agile, robust, AI-ready target state data architecture
• Lead the design of systems and pipelines that ingest, cleanse and normalize diverse internal and external datasets, and add structure to unstructured data for analytical and AI use, setting patterns that other engineers and teams adopt
• Partner with senior business, product and IT leaders to shape requirements, anticipate future needs, and translate ambiguous business problems into prioritized technical strategy
• Maintain an enterprise-wide view of how data flows across CRM, Broker & Sales, Finance, HR, MDM, ODS, Data Lake and EDW, and identify and resolve cross-domain architectural gaps, duplication and risk
• Drive delivery of data and API solutions, including natural-language and AI-assisted access to governed data, that enable non-technical staff to make data-driven decisions
• Lead build-versus-buy and platform-pattern decisions (for example, EDP-provided capabilities versus custom builds), documenting trade-offs, cost and risk for senior stakeholders
• Lead POCs that validate solution proposals, and influence platform architects, product managers and software engineers to adopt proven approaches
• Provide technical leadership and mentorship to engineers, set engineering standards and conduct design and code reviews, raising the capability of the wider data engineering community
• Guide colleagues and partner teams, including external vendors and delivery partners, in migrating to modern platforms, holding them to agreed architectural and quality standards
• Own platform reliability, performance and cost efficiency for the data products and pipelines in scope

AI DATA ENGINEERING

• Architect AI-ready pipelines that curate and version data for retrieval, evaluation and agentic workflows, including document parsing, chunking, embeddings and vector search for RAG, and establish them as reusable enterprise patterns
• Define the API and MCP-based foundations that give AI agents governed access to enterprise data, and the standards for how teams build on them
• Set enterprise standards for data quality, lineage, observability and evaluation frameworks that measure the accuracy of AI-generated answers against trusted data
• Define governance and access-control patterns (row/column-level security, masking) so AI solutions respect data classification and privacy requirements, working with security, privacy and governance stakeholders
• Lead the path from AI prototype to production-grade, monitored solutions in partnership with data science and business teams, including operating models, ownership and support

SEMANTIC LAYER (DATABRICKS)

• Own the strategy and architecture of the governed semantic layer in Unity Catalog, including curated views, certified metric definitions and centralized business logic, so that business logic lives in governed objects rather than in individual tool configurations
• Set standards for configuring and tuning Databricks Genie spaces (instructions, sample queries, entity matching, naming conventions), and lead accuracy benchmarking and reliability improvement
• Establish semantic layer standards, documentation, versioning and certification processes, and secure alignment with Finance, HR and other business owners on source-of-truth definitions
• Drive adoption of the semantic layer across domains and teams, resolving conflicting definitions and escalating where business ownership is unclear

REQUIRED QUALIFICATIONS

• 10+ years of experience in data engineering or closely related roles, including experience operating at a senior level
• Extensive, hands-on Databricks experience (Delta Lake, Unity Catalog, Spark SQL, PySpark, Workflows) with expert-level SQL and Python skills
• Proven track record designing and delivering a semantic layer in Databricks (Unity Catalog views, metric views and/or Genie configuration) adopted across multiple teams or domains
• Demonstrated experience architecting batch and streaming pipelines and lakehouse/medallion data models at enterprise scale
• Experience designing and governing REST APIs (FastAPI or Flask) with OAuth-based authentication and authorization, including API gateway patterns (such as Azure APIM)
• Strong working knowledge of AI data engineering: embeddings, vector search, RAG, LLM agents, context engineering and AI evaluation, with experience taking solutions into production
• Deep experience with data governance, lineage, quality and security in a large enterprise environment
• Experience with cloud platforms (Azure preferred), CI/CD and infrastructure practices, with accountability for reliability and cost
• Demonstrated technical leadership: mentoring engineers, leading design reviews, and influencing architecture and priorities across teams without direct authority
• Strong written and verbal communication, able to explain complex trade-offs to senior technical and non-technical stakeholders

PREFERRED QUALIFICATIONS

• Experience with Genie or similar text-to-SQL tools, including accuracy benchmarking and methodology validation
• Experience with MCP or agent frameworks for enterprise data access
• Experience leading work across external vendors or delivery partners
• Experience in financial data domains (ledger, AR, FP&A) or in finance data-sharing and provisioning models

This position does not provide visa sponsorship. Candidates must be authorized to work in the United States without sponsorship.

Expected compensation for this position:

Final compensation packages are determined by various considerations including but not limited to candidate qualifications, location, market conditions, and internal considerations.

Location:

If this job description resonates with you, we encourage you to apply, even if you don't meet all the requirements. We're interested in getting to know you and what you bring to the table!

Personalized benefits that support personal well-being and growth:

JLL recognizes the impact that the workplace can have on your wellness, so we offer a supportive culture and comprehensive benefits package that prioritizes mental, physical and emotional health. Some of these benefits include:

• 401(k) plan with matching company contributions
• Comprehensive Medical, Dental & Vision Care
• Paid parental leave at 100% of salary
• Paid Time Off and Company Holidays
• Early access to earned wages through Daily Pay

At JLL, we harness the power of artificial intelligence (AI) to efficiently accelerate meaningful connections between candidates and opportunities. Using AI capabilities, we analyze your application for relevant skills, experiences, and qualifications to generate valuable insights about how your unique profile aligns with the specific requirements of the role you'repursuing.

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