Data Platform Engineering Lead

Meet the team:

The Data Platform team, part of the Autonomy Software organization, is a collective of applied data scientists and data engineers responsible for building robust data pipelines to keep tabs on our robot software. We accomplish this using our in-house data platform, sponsoring cloud-native workflows, and championing data analytics tools across our partner teams. As the Data Platform Engineering Lead, you'll set the technical direction of our rapidly scaling data stack, staying hands-on to build its most critical pieces while growing the team of engineers behind it.

 

Roles & Responsibilities:

• Own the technical roadmap for our data platform, balancing near-term delivery against long-term scalability.
• Develop analytics and data accessibility solutions for internal engineering teams as well as external stakeholders.
• Set a high bar of technical excellence for data quality, validation, governance, and observability across the data lifecycle.
• Mentor and grow a team of analytics engineers and data engineering by guiding technical decisions, reviewing code and designs, and supporting career development.
• Partner with engineering leadership on planning, prioritization, and headcount.
• Collaborate cross-functionally with both technical and non-technical customers to platform new data analytics workloads.
• Research, evaluate, and integrate cutting-edge big data technologies to enhance our platform capabilities and influence build-vs-buy decisions.

 

Knowledge, Skills and Abilities:

• Degree in Computer Science, Analytics, Engineering or a related field.
• Management experience building and leading engineering teams is a must.
• Extensive experience building and operating production data platforms, with a track record of technical ownership over major systems.
• Proficiency with big data technologies (e.g., Spark, Hadoop, Hive, dbt).
• Proficiency with workflow orchestration tools (e.g., Airflow, Argo Workflows).
• Proficiency with multi-language build systems (e.g., Bazel, CMake) and containerization technologies (e.g., Docker, Kubernetes).
• Proficiency with cloud platforms (e.g., AWS, Azure, GCP).
• Expertise in Python and Shell.
• Expertise in SQL and/or SQL-like query languages.
• Expertise in version control systems (e.g., Git).
• Expertise in configuration languages (e.g., YAML, CUE).

Meet the team:

The Data Platform team, part of the Autonomy Software organization, is a collective of applied data scientists and data engineers responsible for building robust data pipelines to keep tabs on our robot software. We accomplish this using our in-house data platform, sponsoring cloud-native workflows, and championing data analytics tools across our partner teams. As the Data Platform Engineering Lead, you'll set the technical direction of our rapidly scaling data stack, staying hands-on to build its most critical pieces while growing the team of engineers behind it.

 

Roles & Responsibilities:

• Own the technical roadmap for our data platform, balancing near-term delivery against long-term scalability.
• Develop analytics and data accessibility solutions for internal engineering teams as well as external stakeholders.
• Set a high bar of technical excellence for data quality, validation, governance, and observability across the data lifecycle.
• Mentor and grow a team of analytics engineers and data engineering by guiding technical decisions, reviewing code and designs, and supporting career development.
• Partner with engineering leadership on planning, prioritization, and headcount.
• Collaborate cross-functionally with both technical and non-technical customers to platform new data analytics workloads.
• Research, evaluate, and integrate cutting-edge big data technologies to enhance our platform capabilities and influence build-vs-buy decisions.

 

Knowledge, Skills and Abilities:

• Degree in Computer Science, Analytics, Engineering or a related field.
• Management experience building and leading engineering teams is a must.
• Extensive experience building and operating production data platforms, with a track record of technical ownership over major systems.
• Proficiency with big data technologies (e.g., Spark, Hadoop, Hive, dbt).
• Proficiency with workflow orchestration tools (e.g., Airflow, Argo Workflows).
• Proficiency with multi-language build systems (e.g., Bazel, CMake) and containerization technologies (e.g., Docker, Kubernetes).
• Proficiency with cloud platforms (e.g., AWS, Azure, GCP).
• Expertise in Python and Shell.
• Expertise in SQL and/or SQL-like query languages.
• Expertise in version control systems (e.g., Git).
• Expertise in configuration languages (e.g., YAML, CUE).