Sr. Data Engineer Manager

Lead, coach, and develop a team of data engineers, creating an inclusive and high-performing environment where team members grow their technical depth, expand ownership, and deliver meaningful business impact.

Drive the design and delivery of scalable real-time and batch data pipelines and platforms that enable trusted, timely, and high-volume data consumption across product, analytics, and machine learning use cases.

Define and execute the roadmap for modern data engineering capabilities, including streaming, orchestration, transformation, observability, and cloud-native platform development using technologies such as Kafka, Spark, Flink, Airflow, dbt, Databricks, and AWS.

Partner cross-functionally with Product, Data Science, Analytics, and Engineering teams to prioritize investments, translate evolving requirements into robust technical solutions, and deliver data products that improve customer and business outcomes.

Raise the engineering bar by establishing strong practices for architecture, code quality, testing, documentation, operational readiness, and data reliability, while proactively improving scalability, performance, and cost efficiency.

Own execution across major initiatives from planning through production operations, balancing near-term delivery with long-term platform health and influencing technical decisions that improve reuse, maintainability, and speed across teams.

Experience leading and developing engineering teams, with a track record of delivering complex data, platform, or backend initiatives in fast-paced and highly collaborative environments.

Strong technical foundation in data engineering or software engineering, including experience with distributed systems, large-scale batch and streaming pipelines, and cloud-based data platforms.

Hands-on knowledge of technologies such as Kafka, Spark, Flink, Databricks, Airflow, dbt, AWS, or comparable modern data engineering tools, with the ability to guide architecture and support strong technical execution.