Sr. Manager Program, Enterprise Data

Panera Bread•Newton, MA

2d•$155,477 - $217,667•Onsite

About The Position

The Data Program Manager is responsible for leading the end-to-end delivery of Panera's cross-domain data and analytics programs. This role blends strong program management discipline with genuine data fluency, ensuring programs are planned realistically, tracked transparently, and delivered on time with zero tolerance for unflagged delays, especially on high-ROI initiatives. This leader partners closely with Data Engineering, Analytics, MDM Data Governance, and business stakeholders to prioritize work, hold teams accountable, and keep leadership informed at every stage.

Requirements

Bachelor's degree in Business, Information Systems, Data Analytics, or related field required.
15+ years of program/project management experience, with a strong track record specifically in data or analytics programs.
Solid working knowledge of data architecture, ETL/pipeline concepts, and cloud data platforms (GCP or equivalent).
Proven ability to hold cross-functional teams accountable to delivery commitments, with a track record of zero silent delays.
Experience managing coordinated delivery across multiple data teams (engineering, analytics, governance, platform).
Experience with data governance principles (data quality, stewardship, access/compliance).
Proficiency with Jira, Confluence, or similar tools for program/project tracking and status reporting.
Excellent executive communication skills — comfortable delivering difficult updates to senior leadership, early and transparently.

Nice To Haves

Advanced degree a plus.
PMP, Agile/Scrum certification, or equivalent program management credential preferred.
Strong negotiation skills able to push back on unrealistic customer/stakeholder asks while maintaining productive relationships.
Experience in retail, restaurant, or QSR industries preferred but not required.

Responsibilities

Own end-to-end delivery of enterprise data programs, from scoping and prioritization through execution, launch, and post-delivery validation.
Manage program timelines, budget, resourcing, and interdependencies across multiple concurrent workstreams and technical domains.
Coordinate and manage tasks across all data teams (engineering, analytics, platform, governance) to maintain a single, consistent view of program status.
Apply extra rigor to large, high-ROI projects closer tracking, more frequent checkpoints, and lower tolerance for drift.
Facilitate prioritization discussions across competing stakeholder demands, balancing business urgency with technical feasibility and team capacity.
Push back appropriately on customer/stakeholder requests that exceed reasonable scope, timeline, or capacity, using data and risk analysis to negotiate a sustainable, realistic path.
Partner with data engineering and technical leads to build and validate accurate, defensible estimates informed by real understanding of data complexity.
Hold delivery teams accountable to commitments through regular check-ins, program reviews, and clear ownership tracking no task should silently fall behind.
Ensure any at-risk or delayed task is flagged immediately, with a clear cause, owner, and recovery plan.
Maintain program visibility and tracking in Jira (or equivalent tool): epics, sprints, backlog grooming, and reporting dashboards.
Produce regular, consistent status reports across all workstreams and teams, giving leadership a single source of truth on program health.
Communicate clearly and transparently with senior leaders on status, risks, and delays, surfacing problems early and tailoring messaging for executive audiences.
Treat transparency as a core operating principle — no team or task should be quietly behind schedule.
Proactively identify risks, blockers, and delivery slippage across teams, escalate to the right stakeholders and leadership in a timely manner.
Lead governance routines like status reporting, risk/issue tracking, and steering committee updates.
Bring working knowledge of data architecture, pipelines, and platforms (including GCP or equivalent cloud data platforms) to make informed delivery, scope, and risk decisions.
Understand data quality concepts well enough to identify gaps, question assumptions, and ensure validation isn't skipped under schedule pressure.
Speak credibly with data engineers and analysts about technical tradeoffs without needing translation from a technical lead.
Support and help enforce data governance standards, including data quality, ownership, access controls, and compliance.
Partner with data governance/stewardship teams to ensure programs align with enterprise data policies.