Data Tech Lead
Marsh McLennan (Oliver Wyman, Mercer) · Chicago, United States · 8d ago
Staff / Lead / PrincipalHybridData & Analytics$139k – $278k
Company:
MarshDescription:
This is a hands-on leadership role. You lead a squad of data engineers and you also build things yourself. You're the person who can whiteboard a data model in the morning, pair with an engineer on a tricky pipeline in the afternoon and present a delivery plan to a business stakeholder before end of day. You don't just point; you drive.
You'll own technical delivery for priority initiatives within Marsh’s Innovation & Data Office (IDO). Your first assignment will likely be in one of our Mercer businesses (Wealth or Careers), but the expectation is fungibility, not permanent domain alignment. As IDO's priorities evolve, so will yours. The constant is the quality of your engineering leadership, not the label on the work. Your squad will be a mix of employees and contractors, and you'll be responsible for making that team perform as a unit: setting standards, unblocking problems, mentoring engineers, and holding the bar on quality. The work lands in Databricks. The outcomes land in the business.
The key distinction: This is not a people-management role that happens to involve data. It's a hands-on technical leadership role where managing people is one of the things you do, alongside designing architecture, writing code, reviewing PRs, and solving hard data problems. If your last few years have been spent in meetings about data rather than working with data, this isn't the right fit.
Technical Lead, Data
We will count on you to:
- Lead a delivery squad. You'll run a pod of 4-8 data engineers (employees and contractors) assembled against a business outcome. You set the technical direction, decompose work, manage sprint-level execution, and are accountable for what the squad ships. When the initiative concludes or transitions to steady state, you and your engineers move to the next priority.
- Build with your hands. You write code. You build pipelines. You debug production issues. You're comfortable in SQL, Python, and Spark, and you use them regularly, not as a party trick, but as your primary tools. When something is broken or behind, you can step in and do the work, not just escalate it.
- Design before you build. You think in terms of data models, medallion architectures, ingestion contracts, and reusable patterns. You design solutions that are maintainable and that the next engineer can understand without an archaeology expedition. You care about schema evolution, data quality checks, idempotency, and lineage, not because someone told you to, but because you've been burned when those things were missing.
- Engage with the business. You're not building pipelines into a void. You'll work directly with Engagement Leads and business stakeholders to understand what they need, why they need it, and what "done" looks like. You translate business problems into data solutions and push back when what's being asked for doesn't make technical sense. You're comfortable in a room with non-technical leaders and can explain complexity without hiding behind jargon.
- Drive engineering standards. CI/CD, version control, testing, code review, documentation: these aren't aspirational for you, they're how you've always worked. You'll bring these practices to your squad and raise the bar for everyone around you. You care about developer experience and operational excellence, not just feature delivery.
- Operate on a modern stack. The platform is Databricks (Unity Catalog, Delta Lake, Spark, medallion architecture) running on AWS and Azure. You'll work with infrastructure-as-code, Git-based workflows, and pipeline orchestration tooling. If you've spent the last several years in the Databricks ecosystem, great. If you've built equivalent capabilities on other modern platforms and can ramp quickly, that works too.
What you need to have:
- 5+ years of hands-on data engineering experience, with at least some time leading or mentoring other engineers
- Strong proficiency in SQL, Python, and Spark, used recently and regularly, not years ago
- Real experience with cloud data platforms (Databricks strongly preferred; Snowflake, Synapse, or similar accepted) on AWS, Azure, or GCP
- Demonstrated ability to lead a mixed team of employees and contractors: setting expectations, managing performance, building cohesion across employment types
- Fluency with modern engineering practices: Git, CI/CD, automated testing, infrastructure-as-code, DataOps
- The instinct to understand why something is being built, not just how. Intellectual curiosity about the business domain you're working in
- Clear communication skills; you can explain a technical tradeoff to a business leader and a business requirement to an engineer with equal ease
What makes you stand out:
- Experience with Databricks specifically: Unity Catalog, Delta Lake, medallion architecture, Databricks workflows
- Experience with dbt or similar transformation frameworks
- Familiarity with data governance patterns: lineage, access controls, metadata management, data quality frameworks
- Prior work in financial services, insurance, benefits, or consulting environments
- Experience with document or unstructured data ingestion pipelines
- Exposure to LLM-based tooling, AI-assisted development, or analytics engineering
What success looks like:
- Your squad ships. Pipelines go to production. Data lands in silver and gold. Business users consume it. You're measured by outcomes delivered, not activity performed.
- Your engineers grow. People on your team get better, technically and professionally. Contractors ramp fast because you've built clear standards and onboarding patterns.
- Each initiative leaves things better than it found them. Reusable assets (curated datasets, documented schemas, tested pipelines) accumulate so the next project starts from a stronger baseline, regardless of domain.
- Business stakeholders trust you. They come to you not just for status updates, but for advice on what's possible and what's worth doing. You've earned credibility by delivering, not by presenting.
- You raise the engineering bar. Code quality, testing coverage, and operational reliability improve across the squad. Standards you set become the ones others adopt.