Senior Manager, Data Engineering

JLL Technologies is a specialized group within JLL. At JLL Technologies, our mission is to bring technological innovation to commercial real estate. We deliver unparalleled digital advisory, implementation, and services solutions to organizations globally. Our goal is to leverage technology to increase the value and liquidity of the world’s buildings, while enhancing the productivity and happiness of those that occupy them.

The Data Engineering team builds and operates the data products and pipelines that power JLL’s Corporate Functions, including areas such as finance, HR, Legal, Compliance and shared services. Working on the Enterprise Data Platform (EDP), we turn internal enterprise data into trusted, governed, and increasingly real-time assets that leaders and teams across the firm rely on for reporting, planning, and operational decisions.

What this job involves

As Senior Data Engineering Manager, you will lead multiple related scrum teams of data engineers responsible for Corporate Functions data – from ingestion and streaming pipelines through curated, governed data products. Your teams own the reliability, quality, and timeliness of the data that underpins enterprise reporting, financial and workforce analytics, and shared-services operations.

You will own delivery and direction for the Corporate Functions data engineering function: setting priorities across teams, allocating resources, guiding technical design, and partnering with product, analytics, and business leaders to translate needs into a clear roadmap. You will be accountable for team outcomes, data quality standards, and the continuous improvement of engineering practices.

The ideal candidate blends the technical credibility of a Lead Data Engineer – hands-on depth in Python, Azure, and streaming architectures – with proven people leadership, and the ability to build trust with senior business stakeholders.

What you’ll get to do

As Senior Data Engineering Manager, your “customers” include leaders and teams in Finance, HR, Legal, Compliance and other Corporate Functions, data analysts and BI teams, product managers, and fellow platform and governance teams across JLL Technologies. Your mission is to lead the Corporate Functions data engineering function – defining the roadmap, operating model, and quality standards that make data trusted, timely, and reusable.

This is an opportunity to shape how streaming and governed data enable faster, better-informed decisions across the enterprise – and to grow a high-performing engineering organization while doing it.

Key responsibilities

Technical Leadership: Provide hands-on technical direction for batch and streaming data pipelines built on Azure, Python, and Spark. Guide architecture and design reviews, set engineering standards for reusable frameworks, and stay close to critical technical risks.

Real-Time and Streaming Data Delivery: Lead the design and operation of event-driven pipelines using technologies such as Kafka and Spark Streaming, so that financial, workforce, and operational data reaches downstream consumers with the latency and reliability the business requires.

Data Governance and Quality: Own data quality, lineage, cataloging, and stewardship practices for Corporate Functions data domains, including sensitive finance and HR data. Define measurable quality standards, automate validation and monitoring in pipelines, and partner with enterprise governance and security teams to ensure compliance with JLL data policies and access controls.

Delivery Management: Set priorities and manage execution across multiple scrum teams. Balance roadmap commitments, technical debt, and operational demands; maintain predictable delivery; and adapt plans as business needs evolve.

Platform Alignment and Modernization: Align team deliverables with the Enterprise Data Platform strategy. Drive adoption of platform standards, ingestion patterns, and cloud-native services, and contribute to modernization of legacy data assets.

Reliability and Operational Excellence: Establish CI/CD, observability, and incident management practices that keep pipelines healthy. Ensure teams meet service-level expectations and learn from production issues through blameless reviews.

Cross-Functional Leadership: Collaborate with product, analytics, data science, platform, security, and governance teams to deliver end-to-end data solutions. Resolve dependencies, negotiate shared priorities, and keep teams aligned on outcomes.

Team Development and Mentorship: Recruit, coach, and retain strong engineers and team leads. Set clear expectations, provide regular feedback, build career growth paths, and foster a culture of inclusion, ownership, and continuous learning.

Stakeholder Management: Build trusted relationships with Corporate Functions business and technology leaders. Communicate status, risks, and trade-offs clearly, manage expectations on scope and timelines, and ensure engineering work maps to business value.

Resource and Budget Planning: Partner with leadership on headcount, vendor, and cloud cost planning for your teams, optimizing talent utilization and cost efficiency across products and initiatives.

Who you are

You are a people-first engineering leader who has grown from strong hands-on data engineering roots. You earn credibility with engineers by understanding the technical detail – from distributed processing to data modeling trade-offs – while inspiring them through clear direction, coaching, and high standards. You navigate competing priorities with composure, explain complex topics plainly to executives, and hold yourself accountable for both team outcomes and the quality of the data your teams deliver. You are energized by turning ambiguous business needs into reliable, governed data products.

Required Qualifications

• People management: 2+ years directly managing data engineers or technical individual contributors; experience setting priorities, allocating work, and coaching teams; managing managers is not required.
• Data engineering experience: 7+ years in data engineering, including multiple large and complex projects delivered end to end.
• Technical depth: Advanced Python and SQL; strong experience with Azure data services and distributed processing (e.g., Spark/PySpark); experience building reusable frameworks.
• Streaming: Hands-on experience designing or leading real-time data pipelines with Kafka, Spark Streaming, or equivalent technologies.
• Data governance and quality: Working knowledge of data quality frameworks, lineage, metadata management, and data governance practices.
• Delivery: Experience managing or training teams of data engineers and setting priorities across multiple data engineering workstreams in an agile environment.
• Communication: Strong written and verbal communication skills, with the ability to engage both technical and business stakeholders.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience with corporate data domains such as finance, HR, or shared services (e.g., ERP, HRIS, or enterprise reporting data), or in commercial real estate.
• Experience with NoSQL, graph, or multi-model databases (e.g., CosmosDB, MongoDB) and complex cross-system integrations.
• Familiarity with data cataloging and governance tooling, and with CI/CD and infrastructure-as-code practices.
• Experience with modern lakehouse or data platform architectures and enterprise data platforms.
• Exposure to applying AI/ML or LLM-based capabilities to data engineering and data quality workflows.

Key attributes

Leadership: Builds and retains strong teams; delegates effectively while staying close to critical technical risks.

Stakeholder Partnership: Navigates competing priorities with poise; negotiates scope and timelines with data-driven rationale.

Judgment: Makes sound trade-offs among quality, speed, cost, and risk.

Communication: Explains technical concepts clearly to executives; listens for business constraints that should shape engineering decisions.

Ownership: Accountable for team outcomes, platform health, and continuous improvement of engineering practices.