About The Role

Are you curious about how marketing data turns into insights that drive business decisions? Do you enjoy working hands‑on with data while partnering closely with business stakeholders? Are you looking to deepen your skills in analytics engineering, data modeling, and modern data platforms?

As we continue to grow our data capabilities at Baird, we are seeking a Data & Analytics Engineer (DAE) with a focus on Marketing Analytics. This role is part of our IT Data & Analytics organization and supports marketing‑focused use cases such as campaign performance, customer engagement, and channel analytics. The ideal candidate blends data engineering expertise with business curiosity, strong communication skills, and hands-on experience enabling analytics for Marketing stakeholders.

This role is based in Milwaukee, WI and works closely with Marketing, Analytics, Architecture, and Delivery teams.

The Impact You’ll Make

Marketing Analytics & Business Support

• Partner with Marketing teams and analysts to support analytics needs related to campaigns, customer engagement, and performance reporting.
• Help translate marketing questions into clear data requirements, datasets, and metrics.
• Support analytics use cases such as campaign reporting, segmentation, funnel analysis, and customer insights.
• Build familiarity and trust with Marketing Teams about marketing data sources and how they are used across the organization.
  
  

Data Engineering & Data Management

• Contribute to the design, build, and maintenance of data pipelines that ingest and transform marketing data.
• Apply data modeling skills (3NF and dimensional) to support analytics‑ready datasets.
• Perform data analysis and profiling to understand source data and support quality outcomes.
• Develop and validate source‑to‑target mappings and transformation logic.
• Implement and test end‑to‑end data solutions under the guidance of senior engineers.
• Follow established practices to ensure sensitive data is protected and handled appropriately.
  
  

Analytics Enablement & Delivery

• Support data discovery efforts and help prototype datasets that bring together multiple data sources.
• Leverage existing tools to enable reporting and visualization for Marketing users.
• Document datasets and transformations to support usability and adoption.
• Deliver work incrementally while balancing changing priorities.
  
  

Collaboration, Learning & Growth

• Collaborate with delivery team members, architects, and business partners.
• Communicate clearly about progress, risks, and dependencies.
• Learn and apply Baird data standards, tools, and best practices.
• Seek feedback and coaching from senior Data & Analytics Engineers.
• Continuously build skills through training, documentation, and hands‑on experience.
  
  

What You’ll Bring To Baird

• 5–7 years of experience delivering data and analytics solutions in a collaborative environment.
• Experience with data engineering, analytics, or BI development.
• Strong SQL skills and familiarity with relational data concepts.
• Experience performing data analysis, profiling, and validation.
• Ability to work with business partners to understand and support analytics needs.
• Curiosity, strong problem‑solving skills, and a desire to grow technically.
  
  

Technical Experience (Representative, Not Exhaustive)

• Databases / Platforms: SQL Server, Snowflake, Azure SQL Database, Azure Data Lake.
• Data Integration: SSIS, dbt, Azure Data Factory, or similar tools.
• Query & Development: SQL, T‑SQL, Azure Data Studio.
• BI & Analytics Tools: Power BI, Alteryx, or similar.
• Data Modeling & Governance: Basic 3NF and dimensional modeling, data mapping, data profiling, data quality concepts.
• Data Formats: CSV, JSON, XML, Parquet.
  
  

Highly Preferred (Marketing Analytics)

• Exposure to and experience with marketing or customer data (campaigns, digital engagement, CRM, or customer interaction data).
• Interest in marketing measurement concepts such as attribution, segmentation, or funnels.
• Experience working in a regulated or data‑sensitive environment.