Data and AI Engineer

Brown Gibbons Lang & Company•Chicago, IL

4h•$120,000 - $150,000•Hybrid

About The Position

Brown Gibbons Lang & Company (BGL) is a leading independent investment bank and financial advisory firm focused on the global middle market. We advise private and public corporations and debt and equity sponsors on mergers and acquisitions, capital markets, financial restructurings, valuations and opinions, real estate, and other strategic matters. BGL has investment banking offices in Boston, Chicago, Cleveland, Los Angeles, and New York, and real estate offices in Chicago and Cleveland. The firm is also a founding member of REACH Cross-Border Mergers & Acquisitions, enabling BGL to service clients in 30 countries around the world. On every engagement, our clients receive senior-level attention from experienced bankers who bring a wealth of industry knowledge, transaction expertise, and deep relationships with key players in a broad range of industries. Our success and growth is due to the expertise, passion, and commitment of our team. We value our employees and invest in their development, recognizing that their talent is the foundation of the exceptional service we deliver to BGL clients. The Data & AI Engineer is a hands-on builder who develops BGL’s data platform, analytics, and AI integrations. The role is a shared resource reporting to both the Director of Information Technology and the Director of Enterprise Intelligence & AI: the IT function provides the governed platform and security controls, while the Enterprise Intelligence & AI function drives the analytics and AI use cases the engineer builds and holds the administrative reporting line. The role is designed to evolve with the firm’s AI maturity: from day one it combines data platform work with hands-on AI enablement — integrations, retrieval pipelines, and support for AI tools already in deployment — and grows into building the firm’s AI applications as the roadmap advances.

Requirements

4+ years of hands-on data engineering experience in production environments.
Strong SQL and Python; demonstrated experience building and operating Snowflake-based platforms and managed ELT (Fivetran or comparable).
Hands-on Tableau experience with an emphasis on published data source architecture, connectivity, and performance; dashboard development proficiency; site administration experience preferred, or the aptitude to take ownership of it quickly.
Experience integrating cloud AI services (Azure OpenAI / Azure AI services or comparable) into data workflows.
Working knowledge of data governance, access control, and security practices; comfort operating within compliance guardrails.
Strong documentation and communication skills; able to serve stakeholders with transparency.

Nice To Haves

Experience in financial services or another regulated industry.
dbt or comparable transformation framework; CI/CD for data workloads; infrastructure-as-code exposure.
Experience with Power BI and/or Microsoft Fabric semantic models and reporting enablement.
Familiarity with Microsoft Purview, sensitivity labels, and DLP as they apply to data and AI workloads.
SnowPro Core, DP-600 (Fabric Analytics Engineer), DP-203, or AI-102 certifications

Responsibilities

Design, build, and maintain ELT pipelines using Fivetran into Snowflake, ingesting line-of-business and SaaS sources; handle schema evolution, monitoring, and failure recovery.
Develop curated, well-documented data models and transformation layers that make firm data consistent, discoverable, and reliable for downstream use.
Implement data-quality checks, testing, and lineage documentation appropriate to a regulated environment.
Build semantic layers and datasets that power reporting, partnering with the Data & Analytics Specialist who develops business-facing reports and dashboards.
Own all technical aspects of the firm’s Tableau environment, including site and user administration, permissions, published data sources and connectivity (Salesforce, Snowflake, SharePoint/OneDrive), extract and refresh schedules, and performance tuning.
Architect and maintain the published data sources, connections, and certified datasets that power the firm’s dashboards; build and support dashboards in partnership with business stakeholders, who own requirements and visual design.
Enable governed self-service analytics with clear definitions, certified datasets, and usage documentation.
Build the data foundations and technical integrations for the firm’s AI initiatives (e.g., retrieval pipelines, API integrations, Azure AI services), as directed by the Enterprise Intelligence & AI roadmap.
As the firm’s data foundation and AI roadmap mature, prototype and build AI-powered solutions on the data platform (e.g., document intelligence, deal and relationship insights, internal copilots and agentic workflows), moving successful prototypes into production within compliance guardrails.
Establish testing and evaluation practices for AI outputs (accuracy, retrieval quality, hallucination monitoring) so AI solutions meet the standards of a regulated firm.
Develop within IT-owned security and compliance controls — sensitivity labels, DLP, audit, and retention — so AI workloads remain examiner-ready.
Instrument and monitor AI and data platform consumption (Snowflake credits, Azure AI usage), reporting cost trends and optimization opportunities.