Senior Manager Portfolio Engineering

Philip Morris International U.S.•Tampa, FL

1d•$160,000 - $200,000•Hybrid

About The Position

The Senior Manager, Portfolio Engineering plays a critical role in enabling the successful execution of the enterprise data product portfolio, serving as the bridge between product strategy and engineering delivery. In this role, you will lead portfolio-level engineering execution across multiple data products, ensuring alignment, scalability, and high-quality delivery through strong engineering practices and agile execution. You will drive coordination across teams, optimize delivery performance, and embed governance, reliability, and continuous improvement into engineering workflows.

Requirements

Legally authorized to work in the U.S.
Commutable distance to Tampa, FL.
8+ years of experience in data engineering, analytics engineering, or data platform delivery; including 3+ years of leadership experience managing or influencing engineering teams in complex environments.
Bachelor’s or Master’s degree in Computer Science, Engineering, Data, or a related field.
Strong expertise in ETL/ELT pipelines, APIs, data modeling, and scalable data architectures.
Hands-on experience with modern data platforms (e.g., Snowflake, Databricks, cloud ecosystems).
Proven ability to manage cross-team dependencies, release cycles, and portfolio-level delivery.
Experience working in agile environments with CI/CD practices in data engineering.
Strong understanding of data governance, metadata, data quality, and compliance integration.

Responsibilities

Translate product portfolio priorities into integrated engineering delivery plans aligned to business outcomes.
Manage cross-product dependencies, sequencing, and release coordination to optimize delivery and time-to-value.
Establish and carry out portfolio-level engineering governance, including standards, design reviews, and release readiness.
Oversee the end-to-end data engineering lifecycle (ingestion, transformation, modeling, and delivery).
Drive adoption of modern data architecture patterns (data products, APIs, lakehouse, data mesh where applicable).
Partner with Product, Architecture, and platform teams to ensure alignment on roadmaps, backlogs, and engineering standards.
Standardize agile ways of working and improve delivery through KPI tracking (e.g., cycle time, reliability, deployment frequency).
Embed data quality, observability, governance, and compliance controls into engineering workflows.
Remove delivery bottlenecks and continuously improve scalability, reliability, and performance of data pipelines and platforms.
Lead, mentor, and develop engineering talent while fostering a culture of accountability and engineering excellence.