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Manager, Data Solutions

Job in Chicago, Cook County, Illinois, 60290, USA
Listing for: HAVI
Full Time, Part Time, Seasonal/Temporary position
Listed on 2026-03-01
Job specializations:
  • IT/Tech
    Data Engineer, Data Analyst, Data Science Manager
Salary/Wage Range or Industry Benchmark: 100000 - 125000 USD Yearly USD 100000.00 125000.00 YEAR
Job Description & How to Apply Below

2 days ago Be among the first 25 applicants

Full/Part-time:
Full time

Job Category:
Analytics

City:
Chicago

HAVI is a global, privately owned company focused on innovating, optimizing and managing the supply chains of leading brands. Offering services in marketing analytics, packaging, supply chain management and logistics, HAVI partners with companies to address challenges big and small across the supply chain, from commodity to customer. Founded in 1974, HAVI employs more than 10,000 people and serves customers in more than 100 countries.

HAVI’s supply chain services are complemented by the customer engagement services offered by our affiliated company The Marketing Store. For more information, please visit

Overview

Innovate and institutionalize data management solutions to continuously enhance the quality of our analytic products in supply chain visibility, optimization, forecasting, and planning. Lead a group of skilled data analysts and data engineers to design, develop, and implement scalable and performant data management and quality assurance solutions to support our products globally. Collaborate with Product, Process, and Technology teams to ensure alignment with overall strategic and commercial needs.

This is a hybrid role based at 345 N Morgan St, Chicago, IL 60607. Candidates must reside in the Chicago metropolitan area. Relocation assistance is not offered at this time.

Responsibilities
  • Responsible for working with the Director of Global Analytics and Insights to define, design, develop, evolve, and implement data processes and solutions globally
  • Responsible for establishing and maintaining data standards and governance
  • Responsible for ensuring continuous data availability and assuring data quality
  • Responsible for building and maintaining a monitoring framework that captures operational KPI for data pipeline health, data quality, and performance
  • Develop a scalable operations model to include documented release procedures, communication, and roadmap
  • Collaborate with Technology to understand current architecture and infrastructure and to drive improvements to better serve the business needs
  • Collaborate with source data application owners to ensure data integrity
  • Manage service levels, ensuring data solutions are fully supported to their SLAs
  • Deliver Traceability service to customers and other key stakeholders
  • Drive thought leadership in areas such as Gen AI/Agentic AI/ML and other recent advancements in data and technology
  • Participating fully and collaborating with the broader analytics teams in developing customer relevant models and solutions
  • Drive customer satisfaction and value creation
  • Train, develop, and mentor other analytics team members
  • Strengthen cross functional relationships
  • Ensure alignment with revenue and profit goals for Global Planning and Analytics
Qualifications
  • Bachelor’s degree in computer science, engineering or closely related field; advanced degree preferred
  • 4+ years of managing, developing, and deploying scalable data management and governance frameworks supporting analytics and operational systems.
  • Proven experience defining and enforcing enterprise data governance policies, including metadata management, data lineage, master data management (MDM), data cataloging, and access control aligned with corporate and regulatory standards.
  • Strong working knowledge of data stewardship, data quality monitoring, and data lifecycle management processes across multiple business domains.
  • Demonstrated expertise in building and optimizing data pipelines and integration workflows leveraging modern cloud technologies (Azure Data Factory, Databricks, Synapse, Spark) within a metadata-driven architecture.
  • Experience collaborating with Data Engineering teams to translate business rules into robust ETL/ELT logic, ensuring consistent and trusted data ingestion, transformation, and distribution.
  • Skilled at data modelling (conceptual, logical, and physical) and schema design for analytical and operational workloads across data lakes and warehouses.
  • Hands‑on experience automating data operations, including pipeline orchestration, validation, alerting, and SLA tracking through Dev Ops tools, CI/CD workflows,…
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