Senior Manager Data Enablement

Philip Morris International U.S.•Tampa, FL

12h•$160,000 - $200,000•Hybrid

About The Position

We are seeking an Engineering Manager, Data Services & Enablement to lead teams building and operating data pipelines, data products, and self-service capabilities, while owning the end-to-end data lifecycle. This role drives the development and optimization of the enterprise data platform, delivering scalable, governed, high-quality data solutions that accelerate time-to-insight, improve reliability, and enable AI/ML and analytics through efficient, self-service access to analytics-ready data.

Requirements

Legally authorized to work in the U.S.
Commutable distance to Tampa, FL.
8+ years of experience in data engineering, data platforms, or analytics engineering, including 3+ years leading and developing high-performing teams.
Bachelor’s or Master’s degree in Information Systems, Computer Science, Engineering, or a related field.
Proven track record building and scaling enterprise data platforms, pipelines, and data products in complex environments.
Strong expertise in data ingestion patterns, ETL/ELT frameworks, distributed processing (Spark, SQL, Python), and cloud platforms (Snowflake, Databricks, or similar).
Deep understanding of data modeling (dimensional, semantic, domain-driven) and modern architecture approaches (data mesh, data fabric, API-driven design), along with experience in BI/analytics tools (e.g., Power BI), data catalog/metadata solutions, and CI/CD for data pipelines.
Experience in CPG, retail, or other highly data-driven industries, with exposure to CRM, ERP, consumer, or supply chain data ecosystems, and familiarity with data governance frameworks, regulatory requirements, and data product management principles.
Strong stakeholder management skills with the ability to translate business needs into technical solutions and measurable outcomes.

Responsibilities

Own the end-to-end data lifecycle, including ingestion (batch, streaming, APIs), transformation (ETL/ELT), modeling, storage, integration, and delivery of data products.
Lead design and implementation of modern data architectures (lakehouse, data mesh, data-as-a-product) to support scalable, composable ecosystems.
Manage, mentor, and scale a team of data engineers; establish best practices, reusable frameworks, and CI/CD standards.
Translate business priorities into technical roadmaps in partnership with product managers, architects, and cross-functional stakeholders.
Enable self-service analytics and data discoverability through tools like Power BI, semantic layers, and data catalogs.
Embed governance, data quality, monitoring, and observability into engineering workflows to reduce defects, latency, and operational inefficiencies.
Drive platform performance, scalability, reliability, and cost optimization with clear SLAs and monitoring standards.
Partner with vendors and evaluate technologies aligned to enterprise data strategy and architecture.