Senior Engineering Manager - Data

What You Will Do:

• Be a visionary leader providing guidance and mentorship to your team and across the organization on data engineering best practices, data frameworks, and robust ETL/ELT pipelines.
• Be responsible for owning large-scale data platforms and data engineering projects developed by your team in each phase – design, implementation, code review, QA, and deployment to production.
• Flex your strength and knowledge on data modeling, data architecture, and high-volume ETL/ELT pipeline development.
• Build a capable team with strong leadership through effective headcount planning, hiring, on-boarding, retention, and continuous improvement.
• Motivate and influence teams to buy into team vision and quarterly roadmap, pushing individuals outside of their comfort zones where necessary.
• Ensure services are built to industry best practices, including observability, architectural patterns, and inter-team dependency mechanisms (like SLOs).
• Work closely with architects, and other teams across the organization to design and implement robust, scalable data solutions and pipelines.
• Collaborate with business leaders to understand core needs when designing data products.
• Champion Data Product principles, Governance, and Discovery across the domain, partnering with other data engineering functions organization-wide.
• Obsessively focus on production readiness for the team including testing, monitoring, deployment, documentation, and proactive troubleshooting.
• Break down complex initiatives into concrete iterative pieces.
• Seek out, define, and evolve best practices within teams’ area of focus as well as initiatives that raise the standards across the organization.
• Provide visibility into team results and bottlenecks, and drive data-driven action plans.
• Work within an Agile culture to foster continuous improvement at the team and departmental level.
• Proven success in mentoring junior engineers to improve standards and reduce defects in dynamic environments.

We Are a Match Because You Have:

• 10+ years of engineering experience, preferably a mix of start-up and large-company backgrounds.
• eComm domain expertise is a plus.
• Hands-on experience driving software transformations in high-growth, scalable environments.
• Prior experience managing data engineering teams and large-scale data engineering projects.
• Mastery in cross-functional consensus building and influencing without direct authority.
• Experienced in architecting and building large-scale, cloud-based data platforms and ETL/ELT pipelines.
• Successful background designing production systems at scale (fault tolerance, reliability, performance, security).
• Excellent communication skills with demonstrated ability to drive teams and influence results.
• Experience with GCP (target platform) and scale experience with AWS/Azure.
• Expert-level exposure to data frameworks, ETL/ELT pipelines, and data governance principles.
• Experience with high-volume async messaging and large-scale relational/NoSQL data stores.
• Deep understanding of data processing and data pipelines.
• Familiarity with common open source data platforms/tools: Kafka, Spark, Flink, Data Warehouses (Snowflake, BigQuery), Data Lakes, Kubernetes, Java/Python, and NoSQL stores.
• Proven experience with containerization (Docker) and Kubernetes (K8s).
• Experience with large-scale stream/batch processing (e.g., Kafka, Spark, Flink, Beam).
• Experience working with relational and non-relational databases.
• Experience with event-driven architectures, DDD, and TDD.
• Experience in system monitoring (e.g., DataDog).
• Familiarity with challenges in productionizing and scaling ML systems.
• Strong foundation in computer science, distributed systems, and data structures.
• Experience leveraging modern IDEs and AI-assisted development tools (e.g., Cursor, GitHub Copilot) to accelerate cycles.