Data & Analytics Lead - IT

Wayne Farms•Oakwood, GA

1d•Onsite

About The Position

The IT Data & Analytics Lead will help build a new centralized analytics function. This role blends business ownership with technical expertise, owning analytics outcomes while designing scalable data models. You’ll shape strategy, partner with stakeholders, and establish trusted data practices that drive meaningful metrics and business decisions.

Requirements

10+ years in relevant work experience
5+ years in analytics, BI, or analytics engineering roles
Experience with cloud data platforms (Snowflake, Databricks, Fabric, etc.)
Strong SQL and data modeling experience
Familiarity with BI tools (Tableau, Power BI, Looker)
Strong communication and stakeholder management skills

Nice To Haves

Experience working in early-stage or building-from-scratch environments
Familiarity with data ingestion tools (Fivetran, Airbyte, etc.)
Exposure to version control (Git) and CI/CD practices
Experience with statistical modeling, predictive analytics, and machine learning to support advanced analytics initiatives
Understanding of data warehousing concepts (fact/dimension models, etc.)
Basic Python

Responsibilities

Analytics Strategy & Business Ownership: Strong business acumen and ability to partner with business stakeholders and leadership to define key metrics, KPIs, and success measures. Translate business problems into prioritized analytics initiatives and analytics outcomes. Own and maintain the analytics roadmap, backlog, and prioritization of projects. Communicate insights, tradeoffs, and data changes to senior stakeholders. Establish and maintain a shared understanding of metrics, data standards, and ownership across the organization.
Analytics Engineering & Technical Leadership: Provide guidance and leadership to consultants during the implementation of the cloud data platform and development of analytics capabilities. Support the design and building of analytics ready data models and datasets. Establish best practices for data modeling, metric definitions, and dataset design. Review and contribute to analytics engineering code with a focus on correctness, performance, and maintainability. Experience with BI and reporting tools (Tableau, Power BI, Looker, etc.). Define and enforce metric definitions and analytic standards. Implement data quality checks, validation tests, and monitoring.
Enablement & Scale: Make self service analytics easier and safer for analysts and business users. Promote data literacy and drive adoption of data-driven decision making across the organization. Define and implement the analytics intake, prioritization, and delivery process. Establish best practices to ensure consistency and trust in data, analytics, and reporting. Establish standards for analytics development, testing, deployment, and documentation. Create and maintain clear documentation of data sources, transformations, definitions, and logic.