Senior Data Engineer

About Us

JLL (NYSE: JLL) is a Fortune 500 leader in commercial real estate services, operating globally with annual revenue in the tens of billions. Within JLL Technologies, our strategic business unit, we harness cutting-edge technology to unlock value and enhance liquidity across the world's built environment.

Our Tel Aviv technology hub drives enterprise-scale AI, data science, and machine learning initiatives that power JLL's multi-billion-dollar business. We design, build, and operate production-grade data and AI systems that inform critical decisions across the organization.

About The Role

We're seeking a Senior Data Engineer to spearhead the modernization of our core data platform. You'll lead the evolution of our large-scale, cloud-based data infrastructure—migrating to a modern Lakehouse architecture, streamlining operations, and establishing best-in-class data quality and observability practices. You'll also explore AI-assisted data collection methods and help architect secure, governed access patterns for internal tools and intelligent agents.

This is a hands-on role offering genuine ownership: you'll shape architecture, drive implementation, and collaborate closely with engineering and product teams to define what an exceptional data infrastructure looks like.

This position offers flexible hybrid working arrangements.

What You'll Do

Modernize the data platform

Lead migration from our current warehouse and orchestration setup to Databricks, creating a clear transformation roadmap

Redesign pipeline architecture to improve operational efficiency and maintainability

Build observable, repeatable, orchestrated workflows that are easier to own and operate

Refactor data structures into cleaner relational models that enhance maintainability, testability, and lineage—while ensuring seamless transitions for downstream consumers

Create comprehensive documentation including migration patterns, phased rollout plans, and operational runbooks

Establish data quality excellence

Design and implement a robust data quality framework across critical datasets using industry-standard tools

Build automated checks for schema validation, volume monitoring, freshness tracking, and distribution analysis

Explore agent-assisted validation for complex scenarios, incorporating human review where judgment is needed

Create actionable alerting with clear ownership so teams can respond confidently to issues

Partner with domain experts to translate business requirements into durable, automated validation

Enable secure, governed data access

Architect how internal users and AI agents access curated data safely through protocols like MCP (Model Context Protocol), with proper authentication, scoping, and auditability

Prototype integrations between the lakehouse and tools engineers and analysts use daily

Help shape our "data as a product" strategy with appropriate access boundaries and governance

Required Experience

What We're Looking For

5+ years in data engineering with end-to-end ownership of production pipelines

Strong proficiency in Spark and distributed processing

Expert-level SQL and experience designing effective warehouse/lakehouse data models

Advanced Python skills for building data infrastructure: factory patterns, abstractions, dynamic DAG generation from configuration, parameterized task definitions, and other programmatic orchestration patterns—not just scripting

Proven track record building or migrating complex batch pipelines, including dependency mapping, phased migration strategies, and maintaining backward compatibility

Practical experience implementing data quality frameworks, building custom tests, establishing monitoring, and responding effectively when issues arise

Excellent communication skills with both technical and business stakeholders

Strong Advantages

Experience with web scraping or API ingestion at scale; curiosity about LLM/agent approaches for data extraction and pipeline maintenance

Familiarity with MCP, LLM tool integration, or internal "data agent" prototypes with appropriate guardrails

Background working with complex domain data including hierarchies, entities, or transactional systems

Strong proficiency with Databricks (jobs/workflows, catalog/governance, Delta or similar formats)

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