Manager, Advanced Analytics and Enterprise Insights

Conagra Brands • Chicago, IL

8d • Hybrid

About The Position

Conagra Brands is building an Enterprise Data Platform to deliver best-in-class data and AI solutions. Join our Advanced Analytics and Enterprise Insights team and help scale our AI Data Engineering capability to accelerate delivery of governed, reliable data products that enable analytics, ML, and GenAI use cases. As a Manager, Advanced Analytics and Enterprise Insights, you will oversee the development and operations of data pipelines and AI-ready data products on our Enterprise Data Platform using modern tools such as Databricks, Snowflake, and Palantir. You will guide the team to build pipelines as code with strong software engineering and DevOps practices, enabling CI/CD automation, observability, and performance optimization across environments. A key focus of this role is ensuring structured and unstructured enterprise data is agent-ready, prepared for secure enterprise retrieval, grounding, and LLM-driven experiences through document processing, metadata enrichment, embeddings/vector indexing, and permission-aware access. This position offers the opportunity to mentor engineers, improve engineering maturity, foresee risks before they impact delivery, and partner across architecture, governance, security, and AI teams to deliver reliable solutions.

Requirements

• Bachelor’s degree in Computer Science, Information Systems, or related field.
• 3+ years of engineering leadership experience (people manager, tech lead manager, or lead with mentorship + delivery accountability).
• 8+ years in data engineering, platform engineering, or similar roles delivering production‑grade pipelines.
• Hands‑on experience with modern data platforms such as Databricks and Snowflake; Palantir or Microsoft Fabric is a plus.
• Strong proficiency with software engineering and DevOps practices: Git workflows, CI/CD automation, automated testing, environment promotion, and observability.
• Experience preparing data for AI/ML, including GenAI readiness across structured and unstructured data.
• Experience with unstructured data processing patterns (extraction, parsing, enrichment, chunking, de‑duplication, versioning).
• Experience implementing governance and security controls including permission‑aware access, PII handling, and auditability.
• Strong problem‑solving mindset with the capability to anticipate issues and guide teams through complex technical challenges.
• Excellent communication and collaboration skills with technical and non-technical partners.

Nice To Haves

• Experience with vector search or retrieval technologies (Azure AI Search, OpenSearch/Elastic, Databricks Vector Search).
• Familiarity with orchestration tools such as Databricks Workflows or Airflow and Infrastructure as Code such as Terraform or CloudFormation.
• Experience defining evaluation signals for retrieval quality (test sets, relevance metrics, feedback loops).
• Experience designing hybrid retrieval and relevance tuning approaches.

Responsibilities

• Lead, mentor, and grow a team of AI Data Engineers, including hiring, onboarding, coaching, and development.
• Oversee delivery outcomes including planning, prioritization, sequencing, risk management, and stakeholder communication.
• Collaborate with Enterprise Architects and Data Architects to shape end‑to-end solution designs aligned to enterprise standards, data models, governance, and platform patterns.
• Translate architectural direction into actionable engineering execution, reusable patterns, backlog decomposition, and delivery guardrails.
• Stay closely connected to code and systems through technical reviews, PR reviews, and support on complex implementations.
• Establish standards for pipelines and workflows as code, including version control, code reviews, automated testing, and reusable components.
• Define and implement CI/CD patterns for data and AI pipelines across environments.
• Ensure structured data is AI‑ready through curated semantic layers, conformed dimensions, metric definitions, and data contracts.
• Ensure unstructured data is AI‑ready via scalable ingestion, parsing, normalization, de‑duplication, chunking, metadata enrichment, and content versioning.
• Oversee embeddings pipelines, vector indexing, lifecycle management, and cost optimization.
• Enable hybrid retrieval patterns (keyword + vector + metadata filtering) and guide relevance tuning strategies.
• Implement permission‑aware retrieval aligned to entitlements, access controls, PII handling, redaction, and auditability.
• Proactively identify and mitigate risks such as schema drift, data quality degradation, stale indexes, and runaway compute costs.
• Improve operational maturity via observability, SLAs, runbooks, incident response, and post‑incident learning.
• Partner with AI/ML teams and product owners to define retrieval contracts for agents, including schemas, tool inputs, grounding expectations, refresh SLAs, and evaluation metrics.
• Maintain documentation standards for data products, AI‑ready datasets, engineering patterns, and operational processes.

Benefits

• Comprehensive healthcare plans, wellness incentive program, mental wellbeing support and fitness reimbursement
• Great pay, bonus incentive opportunity, matching 401(k) and stock purchase plan
• Career development opportunities, employee resource groups, on-demand learning and tuition reimbursement
• Paid-time off, parental leave, flexible work-schedules (subject to your location and role) and volunteer opportunities

What This Job Offers

• Job Type: Full-time
• Career Level: Manager