Lead Developer - Data, AI Wealth Tech Platforms - Chicago, IL

Position: Lead Developer - Data, AI, & Wealth Tech Platforms - Chicago, IL
From Complexity to Clarity Savant is growing fast—and with growth comes complexity.

We’re hiring a Lead Developer for our Data, AI, and Wealth Tech. Platforms to help turn a fragmented ecosystem of systems into a single, trusted platform—one that supports scale, enables AI, and fundamentally improves the advisor experience. This role is about taking something messy and complex and building data infrastructure that actually works.

Behind the scenes, wealth management is weighed down by a problem every advisor knows too well: too many tools, too much friction, and not enough time spent with clients. Advisors are forced to navigate dozens of systems just to do their jobs—and that complexity compounds as the firm grows.

That’s where you come in.

We’re building a unified advisor platform that brings client data, portfolio insights, tax planning, compliance, and AI‑powered intelligence into one place. This is not a proof of concept or a side project. It’s a core platform, directly tied to Savant’s long‑term growth strategy—and the data foundation you build will be part of it from day one.

If you’re an experienced data engineer who wants to build real systems, see your work used in the real world, and help shape how an organization operates at scale, this is a role where you can truly leave your mark.

What You’ll Be Building As our Lead Developer – Data, you’ll own the design, build, and reliability of enterprise‑grade data pipelines that connect Savant’s wealth technology ecosystem into our Microsoft Azure / Fabric data platform—fueling analytics, advisor dashboards, and AI use cases s is a hands‑on, senior role for someone who can step in, make architectural decisions, and execute with confidence.

You’ll be working on things like:

Production data pipelines integrating wealth platforms (Tamarac, eMoney, Salesforce FSC, tax tools, AI note taking tools, and more)
API‑based and file‑based ingestion patterns (REST, SFTP, vendor extracts, near‑real‑time feeds)
Curated, analytics‑ready datasets (dimensional and/or Data Vault-style)
Data quality, reconciliation, monitoring, SLAs, and alerting

Secure, compliant data movement and governance (lineage, access controls, retention)
Preparing structured and unstructured data to support:

AI & ML initiatives

Vectorized content pipelines

Retrieval‑Augmented Generation (RAG) workflows

This role sits at the intersection of data engineering, AI enablement, and real business impact.

How You’ll Work Team:
You’ll be part of the Data Science & AI team, reporting directly to the Director.

Collaboration:

You’ll work closely with partners across BI, Wealth, Compliance, Platform, and Analytics to deliver shared outcomes.

Work Model:
Hybrid, Chicago‑area based. Expect ~1 day per week in the office, with flexibility beyond that.

Schedule:

Flexible overall, but this role supports offshore teams, so occasional early mornings or late evenings may be required to collaborate across time zones.

Leadership & Ownership:
There are no direct reports (yet), but mentorship and technical leadership are expected.

You’ll have autonomy, support, and visibility—without micromanagement. We trust senior engineers to own outcomes, make decisions, and raise the bar.

How Success Is Measured You’ll know you’re winning when:

Data pipelines are reliable, monitored, and hitting SLAsNew platforms integrate faster and cleaner than before

Advisors spend less time navigating systems—and more time with clients

AI and analytics teams can move faster because the data just works

The platform you helped build becomes something the business depends onWho We’re Looking For (Non‑Negotiables)
This is a senior role. We’re very intentional about what “qualified” means here. You must have:7+ years of hands‑on data engineering experience
1.5–2+ years of applied AI / advanced analytics engineering

Not workshops. Not demos. Real implementations

Bachelors degree in Computer Science, Information Systems, Data Engineering, or a related field (or equivalent practical experience).Deep Microsoft Azure experience (required — not AWS‑only, not GCP‑only)
Proven experience building production data pipelines that integrate complex enterprise platforms — ideally…