Senior Data Engineer, Data Analytics
New
J
JobgetherRobotics data infrastructure
Based in the United StatesFull-TimeSenior
SalaryAnnual base compensation ranging from $175,000 to $274,000 USD
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Job Details
- Experience
- At least 5 years of professional experience as a Data Engineer or in a similar role
- Required Skills
- PythonApache AirflowJavaSparkScala
Requirements
- Have at least 5 years of professional experience as a Data Engineer or in a similar role building, deploying, and maintaining production data pipelines.
- Have strong proficiency in Apache Spark or an equivalent distributed data processing framework.
- Have programming experience in Python, Java, and/or Scala.
- Have hands-on experience with orchestration platforms such as Apache Airflow, Dagster, Prefect, or equivalent technologies.
- Know data serialization and storage formats, including Avro, Parquet, and structured or numerical datasets.
- Be familiar with modern data lake table formats and architectures such as Apache Iceberg, Delta Lake, and medallion architecture.
- Understand data modeling, schema evolution, data quality management, and metadata governance.
- Be able to design logical data models and physical data partitions for analytical queries, playback, and root-cause investigations.
- Understand how to structure and manage large-scale datasets generated by distributed systems and physical devices.
- Have experience collaborating with software engineering, hardware engineering, analytics, and data science teams.
- Nice to have experience with data versioning, immutable dataset architectures, computer vision or perception datasets, multimodal data, C++, and data cataloging platforms.
Responsibilities
- Collaborate with robotics software and hardware teams to define, collect, structure, and curate data for analytics, monitoring, and debugging.
- Design, develop, maintain, and optimize ETL pipelines that transform robot logs and telemetry into structured datasets.
- Use Apache Spark, orchestration technologies, and AWS data services to manage production data workflows.
- Own and maintain gold-layer datasets for fleet dashboards, hardware reliability models, operational reporting, and business metrics.
- Establish data freshness SLAs, documentation standards, monitoring systems, and quality controls.
- Implement data validation, schema evolution, and metadata management using internal registries and cataloging systems.
- Collaborate with analytics, reliability, and machine learning teams to design data models for their requirements.
- Optimize data models and physical partitions to improve query performance for analytics, playback, and root-cause analysis.
- Contribute to shared libraries and APIs in Python, Java, or C++ for data capture, serialization, and consumption when needed.
- Promote data architecture, pipeline reliability, dataset versioning, and governance practices across engineering.
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