dComm Data Engineer Manager
Role details
Job location
Tech stack
Job description
Delivery & Execution
- Partner with Data Product Managers to translate product roadmaps into realistic technical plans, providing accurate estimates and identifying risks early.
- Own end-to-end data engineering delivery across regional pods, ensuring high-quality, on-time execution.
- Scale data programs effectively while maintaining strong delivery velocity and platform reliability.
- Establish clear goals, operating rhythms, and expectations, including OKRs, SLAs, and tech-debt management.
- Coordinate dependencies across pods and prioritize work to meet business commitments., * Actively contribute to architecture and technology decisions, ensuring scalable and reusable solutions across markets.
- Lead technical assessments for new market integrations, proactively identifying risks and dependencies.
- Evaluate and introduce tools and practices that improve data quality, engineering efficiency, and developer experience.
- Provide thought leadership on data engineering strategy, governance, and platform evolution., * Define and embed data engineering best practices across coding standards, data modeling, testing, lineage, and pipeline robustness.
- Drive consistency across regional pods to maximize reuse and reduce duplication.
- Own and manage technical debt, balancing short-term delivery with long-term platform health.
Talent & Team Development
- Lead, coach, and develop pod leads and their distributed teams.
- Foster a culture of accountability, collaboration, technical excellence, and continuous improvement.
- Build team capability and capacity to support ongoing international growth.
Collaboration & Stakeholder Engagement
- Work closely with Data Architecture, BI Engineering, Product Engineering, and Platform teams to ensure alignment.
- Represent regional needs in global platform governance and decision-making forums.
- Partner with U.S. data engineering counterparts, sharing knowledge while respecting independent operating models.
- Act as a trusted data engineering leader for senior technical and business stakeholders.
Requirements
Do you have experience in Scalability?, Do you have a Master's degree?, The ideal candidate is a proven technical leader with experience managing distributed teams, comfortable operating at both strategic and hands-on levels, and passionate about building high-quality, scalable data platforms. You are a strong collaborator, coach, and mentor who can drive delivery while elevating engineering standards., * 10+ years of experience in Data Engineering, with at least 3 years leading large, distributed teams across multiple time zones.
- Experience in Retail and/or E-commerce is strongly preferred.
- Proven track record of scaling data platforms and teams across new domains, sources, or markets.
- Hands-on technical background with the ability to review code, challenge architectural decisions, and dive into complex technical details.
- Experience establishing or significantly improving engineering best practices in growing or maturing environments.
- Strong technical expertise in Python, PySpark, SQL, Airflow, dbt, Big Data technologies (Hadoop, Hive, Impala), AWS, and Azure.
- Familiarity with data governance, data quality frameworks, lineage, and metadata management.
- Experience with agile delivery, sprint planning, backlog management, and multi-team coordination.
- Ability to lead through managers and develop engineering leaders.
- Excellent communication skills and the ability to collaborate effectively with technical and business stakeholders.
- Strong prioritization skills, balancing long-term scalability with short-term delivery needs.
- Bachelor's or master's degree in computer engineering, Data Engineering, or a related field.