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.
ATS Match is available
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