Director - Applied Analytics

About the role

We're a large insurance brokerage sitting on a huge amount of data—policy, claims, submission, market, and client data across many lines of business. Most of it lives in different systems, in different shapes, and a lot of it we still go back to the carrier for. We're hiring a Senior Director of Applied Analytics to build the foundation that makes all of that data connected, trustworthy, and usable, including as the context layer underneath the AI we're building on top of it. This is a hands-on building role first, and a leadership role as it grows. Analytics and Data Engineering both report to the same leader, so you'll work closely with the data engineering team from your first week.

What You’ll Do

Build the data foundation in three ways:

• Consolidation. Bring in carrier data like claims information and commission statements, then organize and consolidate it once so our teams stop going back to the carrier for answers we should already have.
• Operational efficiency. Use our data to make the business run better. Cut manual work, speed up processes, and give teams what they need to make faster, smarter calls.
• Sources of truth. Clean up and stand behind the systems the business runs on, starting with Dayforce, so access, reporting, producer numbers, and growth data all trace back to something we trust. This one starts on day one.

Lead the building:

• Build where the data doesn't exist yet. For the initiatives that matter, figure out what data we'd need, then work with engineering and operations to set up a way to start capturing it, so today's gaps become tomorrow's assets.
• Organize the data underneath the AI. Structure, model, and govern the data that feeds our AI experiences so they have real context and know what to do instead of guessing. We want you working at the front edge of AI-first development, using AI tools to build faster and building data applications that go well beyond dashboards.
• Own it end to end. Take a solution from idea through architecture, modeling, testing, and into production on Snowflake and Azure.
• Work closely with Data Engineering. Set shared standards for data models, pipelines, and how things get to production, so the work holds up and can be reused instead of rebuilt every time.

Set direction and grow the function:

• Make the case. Turn the work in front of you into clear, sized proposals that leadership can actually prioritize and fund.
• Build first, then build a team. Early on, you'll do the work yourself and prove it out. As the results justify it, you'll hire, mentor, and lead a team of data engineers, analytics engineers, and analysts, and set the bar for how they work. Leaders here stay in the work while they lead it, so expect to be a player-coach for the long run, not just the first year.
• Show the impact. Put real metrics on what your work is doing for the business, and report it to leadership.
• Keep it responsible. Make sure everything you build respects data governance, client confidentiality, and the rules that come with insurance data, like usage rights and the handling of PII and PHI.

Qualifications

• 10+ years in data engineering, data architecture, analytics, or similar roles, with a mix of hands-on building and leading people. You're a real builder who can also lead. You can design and ship a solution yourself, quickly, without waiting on a team, and you have the range to grow into leading one. If you only manage, or only build, this probably isn't the right fit. We need both.
• A track record of building data foundations that other teams and systems now depend on: ingestion, modeling, consolidation, and cleanup work that held up over time. Not just reports and dashboards.
• Experience setting data strategy, including spotting where data is missing and standing up new ways to collect it for specific goals.
• Experience building data applications and tools that put data in front of business users, and AI systems, in a way that actually gets used.
• Real hands-on experience with Snowflake and the Microsoft Azure ecosystem, including pipeline and orchestration work with tools like Azure Data Factory, Azure Storage, and Azure ML or Databricks.
• Strong data and analytics engineering skills: SQL, dimensional and semantic modeling, and Python or R. You know what it takes to get data into production and keep it reliable once it's there.
• General software and web development experience. You understand how modern applications are designed, built, and deployed, and you can build data applications, not just models and pipelines.
• Hands-on experience with AI and machine learning, and a good read on where the field is going. We especially care about structuring and grounding data so AI systems retrieve the right thing, and about using AI to build faster.
• The ability to explain a data problem in plain business terms and defend a plan to senior leadership.
• Comfort in ambiguity. You can find a path, and create one when it isn't there yet.

Nice to Have:

• Experience in insurance, brokerage, financial services, or another regulated, data-heavy industry.
• Familiarity with insurance data like policy, claims, submissions, commissions, and carrier or market data, and with agency management systems or an HRIS like Dayforce.
• Experience consolidating messy third-party data from outside partners, like carrier files, EDI feeds, or vendor statements.
• A background in data governance, privacy, or compliance as it relates to how data gets used.
• An MBA or an advanced degree in a quantitative field. Relevant, practical experience works just as well in place of a degree.

What Success Looks Like

• First 90 days: Get to know the data and the business priorities. With Data Engineering already in your corner, put together a ranked list of the foundation work that matters most, including where we'd need to start collecting new data, and have the Dayforce cleanup underway.
• 6 months: Ship at least one high-value solution into production yourself, proving the approach and setting the pattern for how analytics and engineering work together.
• 12 months: Point to a set of solutions with real, measurable impact, and use those results to start hiring and building out the team under you.