Sr Manager, Engineering, Data Engineering

Position Title: Senior Manager, Engineering, Data Engineering
Department/Function: Data Engineering 
Reports to: VP, Engineering 
Location: Remote from the U.S. 
Employment type: Full-Time

 #LI-Remote #LI-CW1 

About the role

The Senior Manager, Data Engineering leads the team responsible for building and operating the data capabilities that power LTK's products, reporting, analytics, governance, and emerging AI use cases.

This leader owns the Data Engineering team's technical direction and execution across data architecture, pipelines, data products, data quality, and platform reliability. The role is responsible for evolving LTK's data ecosystem toward well-governed, reusable, and trusted data capabilities that support business needs.

The person in this role will bring strong people leadership, technical judgment, and business acumen. They will translate complex priorities into actionable plans, balance near-term delivery with long-term platform investments, and build a team that consistently delivers reliable, scalable solutions.

How you will make an impact

• Lead, coach, and develop a team of data and software engineers, setting clear performance expectations, supporting career growth, and building a high-performing, inclusive team.
• Support hiring, talent development, and succession planning to strengthen the team's capabilities and long-term effectiveness.
• Define and drive the technical direction for the Data Engineering team's platforms and capabilities, including data architecture, modeling, ingestion, transformation, storage, APIs, and governance.
• Guide the development of scalable, reusable data models and shared capabilities that support multiple business domains and reduce fragmented solutions.
• Partner with Product, Analytics, Finance, Security, Legal/Privacy, TPMO, and Engineering to translate business priorities into clear roadmaps, delivery milestones, and technical tradeoffs.
• Lead execution of key data engineering initiatives, including platform modernization, analytics enablement, data quality improvements, and migration of critical metrics to trusted sources.
• Collaborate with senior engineering leadership on resource planning, investment priorities, organizational needs, and competing business demands.
• Establish and maintain standards for data quality, reliability, observability, security, performance, and cost efficiency.
• Improve engineering efficiency and platform sustainability through automation, documentation, reusable practices, and clear technical ownership.
• Establish effective team practices for planning, technical design, delivery, incident management, and continuous improvement.
• Proactively identify and manage technical and delivery risks, resolve dependencies, and communicate decisions, tradeoffs, and recommendations to stakeholders.

What you will bring to LTK

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field, or equivalent practical experience.
• 8+ years of experience in data engineering, platform engineering, or backend engineering.
• 3+ years of people leadership experience, with demonstrated success developing engineers, managing performance, and building high-performing teams.
• Experience owning technical roadmaps and leading complex engineering initiatives, balancing business priorities with scalability, reliability, security, performance, and cost.
• Strong technical judgment across data architecture, distributed systems, data modeling, data pipelines, cloud platforms, and production operations.
• Experience establishing engineering practices that improve delivery predictability, operational reliability, team effectiveness, and continuous improvement.
• Strong communication, stakeholder management, and business judgment, with the ability to navigate ambiguity, prioritize competing needs, and build alignment across technical and business teams.

Nice to Have

• Experience building or modernizing enterprise data platforms, including developing trusted, reusable data capabilities and applying data governance principles across data quality, lineage, ownership, privacy, and access controls.
• Experience with modern data engineering technologies such as Python or Scala, SQL, Spark, Kafka, Airflow, AWS services, and Snowflake or comparable platforms.