Sr. EM Data Engineering

About Us

Our leading SaaS-based Global Employment Platform™ enables clients to expand into over 180 countries quickly and efficiently, without the complexities of establishing local entities. At G-P, we’re dedicated to breaking down barriers to global business and creating opportunities for everyone, everywhere.

Our diverse, remote-first teams are essential to our success. We empower our Dream Team members with flexibility and resources, fostering an environment where innovation thrives and every contribution is valued and celebrated.

The work you do here will positively impact lives around the world. We stand by our promise: Opportunity Made Possible. In addition to competitive compensation and benefits, we invite you to join us in expanding your skills and helping to reshape the future of work.

At G-P, we assist organizations in building exceptional global teams in days, not months—streamlining the hiring, onboarding, and management process to unlock growth potential for all.

About the position:

As a Senior Engineering Manager at Globalization Partners, you will be responsible for both technical leadership and people management. This includes contributing to architectural discussions, decisions, and execution, as well as managing and developing a team of Data Engineers (of different experience levels).

 

What you can expect to do: 

• Own the strategic direction and execution of initiatives across our Data Platform, aligning technical vision with business goals. Guide teams through architectural decisions, delivery planning, and execution of complex programs that advance our platform capabilities.
  
  
• Lead and grow high-performing engineering teams responsible for the full data and analytics stack—from ingestion (ETL and Streaming) through transformation, storage, and consumption—ensuring quality, reliability, and performance at scale.
  
  
• Partner cross-functionally with product managers, architects, engineering leaders, and stakeholders from Cloud Engineering and other business domains to shape product and platform capabilities, translating business needs into actionable engineering plans.
  
  
• Drive delivery excellence by setting clear expectations, removing blockers, and ensuring engineering teams are progressing efficiently towards milestones while maintaining technical integrity.
  
  
• Ensure adoption and consistency of platform standards and best practices, including shared components, reusable libraries, and scalable data patterns.
  
  
• Support technical leadership across teams by fostering a strong culture of engineering excellence, security, and operational efficiency. Guide technical leads in maintaining high standards in architecture, development, and testing.
  
  
• Contribute to strategic planning, including the evolution of the data platform roadmap, migration strategies, and long-term technology investments aligned with company goals.
  
  
• Champion agile methodologies and DevOps practices, driving continuous improvement in team collaboration, delivery cycles, and operational maturity.
  
  
• Mentor and develop engineering talent, creating an environment where individuals can thrive through coaching, feedback, and growth opportunities. Promote a culture of innovation, accountability, and psychological safety.
• Challenge the Data Platform Quality and Performance by building/monitoring quality KPI and building a quality-first culture

What we are looking for: 

• Proven experience leading geographically distributed engineering teams in the design and delivery of complex data and analytics platforms.
  
  
• Strong technical foundation with hands-on experience in modern data architectures, handling structured and unstructured data, and programming in Python—capable of guiding teams and reviewing design and code at a high level when necessary.
  
  
• Proficiency in SQL and relational database technologies, with the ability to guide data modeling and performance optimization discussions.
  
  
• In-depth understanding of ETL processes and data integration strategies, with practical experience overseeing data ingestion (batch and streaming), transformation, and quality assurance initiatives.
  
  
• Familiarity with commercial data platforms (e.g., Databricks, Snowflake) and cloud-native data warehouses (e.g., Redshift, BigQuery), including trade-offs