Sr Manager Finance & Operations Data Enablement

LSG Sky Chefs

$108K — $130K *
Finance & Insurance
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Finance, Accounting, Economics, Engineering, Data Analytics, or related field required
  • Preferred advanced degree or certification (e.g., MBA, MS)
  • 6-10+ years of combined experience in finance, operations, and analytics in operations-heavy environments
  • Proven track record of creating data-enabled operating models
  • Hands-on experience in developing end-to-end data solutions
  • Experience with modern data platforms like Databricks and Snowflake
  • Strong understanding of operational and financial metrics

Responsibilities

  • Lead the development of LSG's Data, Analytics, and Modeling (DAM) capability
  • Identify and prioritize high-impact use cases across Operations and Finance
  • Develop end-to-end solutions from raw data to actionable insights
  • Design practical data ingestion solutions for priority use cases
  • Build initial DAM capabilities including meaningful data models
  • Translate complex data into clear actionable insights for decision-making
  • Establish best practices for scalable DAM capabilities across the organization

Benefits

  • Hybrid work model providing flexibility
  • Collaborative cross-functional environment
  • Opportunity to lead and influence data strategy
  • Access to external data and AI resources for innovation
  • Positioning within a high-impact industry
Full Job Description
Posting Start Date: 8/6/26

Job Title: Sr Manager Finance & Operations Data Enablement
Job Location: Irving-USA-75039
Work Location Type: Hybrid

Role Purpose Statement

Lead the development and execution of LSG's Data, Analytics, and Modeling (DAM) capability to accelerate data-enabled decision-making across Operations and Finance. This role will bridge business priorities, modern data architecture, and practical execution by rapidly ingesting, modeling, and translating operational and financial data into scalable insights, tools, and use cases that improve performance, reduce dependency on manual processes, and support LSG's broader data-enabled operating model.

Main Accountabilities

Data-Enabled Use Case Delivery
  • Identify, prioritize, and deliver high-impact use cases across Operations and Finance (e.g., labor productivity, revenue capture, costing, and operational performance).
  • Develop end-to-end solutions that move from raw data to actionable insight, enabling faster and more effective decision-making.
  • Ensure outputs are embedded into daily and weekly operating rhythms, driving measurable business impact.


Data Ingestion & Integration (Use Case Driven)
  • Design and implement practical data ingestion solutions for priority use cases using APIs, system extracts (e.g., SAP, CBASE, ARIS), and external tools.
  • Enable rapid access to data by working independently, while maintaining alignment with enterprise data standards.
  • Partner with IT to transition successful ingestion approaches into scalable, enterprise-supported pipelines over time.


Data Analytics & Modeling (DAM)
  • Build and establish initial DAM capabilities, including business-ready data models that translate raw data into meaningful operational and financial insights
  • Apply modern data architecture concepts, including acquisition (landing), domain (harmonization), and product layers, to structure and standardize data.
  • Develop reusable frameworks and logic (e.g., cost models, labor drivers, revenue tracking) that can scale across locations.


Business Translation & Insight Enablement
  • Translate complex data into clear, actionable insights that support operational and financial decision-making.
  • Develop intuitive reporting outputs (e.g., dashboards, tools, summaries) aligned with business workflows and stakeholder needs.
  • Drive adoption by ensuring stakeholders understand both what the data shows and how to act on it.


DAM Capability Development & Cross-Functional Partnership
  • Establish initial patterns, standards, and best practices to enable repeatable and scalable DAM capabilities across the organization.
  • Partner with Operations, Finance, Industrial Engineering, and IT to align data solutions with business priorities and performance drivers.
  • Leverage external data and AI resources to accelerate development and delivery of solutions, while supporting long-term capability build within LSG.


Knowledge, Skills and Experience

  • Bachelor's degree in Finance, Accounting, Economics, Engineering, Data Analytics, or a related field (required)
  • Advanced degree or certification (e.g., MBA, MS, or equivalent) (preferred)
  • 6-10+ years of progressive experience across a combination of finance, operations, and data/analytics roles, ideally in operations-heavy, manufacturing, logistics, or similar environments
  • Proven track record of designing and building data-enabled operating models, translating operational and financial requirements into scalable data, analytics, and decision frameworks
  • Hands-on experience developing end-to-end data solutions across ingestion, modeling, and insight generation
  • Experience working with modern data platforms such as Databricks and/or Snowflake, and familiarity with data ingestion tools (e.g., Fivetran or similar)
  • Working knowledge of modern data architecture concepts, including acquisition (landing), domain (harmonization), and product layers
  • Strong understanding of operational and financial drivers, including cost structures, productivity metrics, and performance management
  • Experience with data visualization and reporting tools (e.g., Power BI or similar), with the ability to translate data into clear, actionable insights
  • Strong analytical, problem-solving, and communication skills, with the ability to engage both executive and operational stakeholders
  • Demonstrated ability to operate in ambiguous environments, building solutions from the ground up and driving cross-functional collaboration
  • Demonstrated ability to operate as a hybrid specialist across data, operations, and finance, rather than within a single functional discipline


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