{"id":624254,"url":"https://alion.io/job/fieldai-data-platform-engineer-data-pipelines","title":"Data Platform Engineer, Data Pipelines","company":{"id":672883,"name":"FieldAI","domain":"fieldai.com","url":"https://alion.io/company/fieldai","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":81,"open_postings":106,"ghost_share":0.226,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":61,"computed_at":"2026-10-20T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Irvine, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":112000,"max_usd":213000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":479},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Kinesis","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Apache Kafka","optional":false},{"name":"BigQuery","optional":false},{"name":"C++","optional":false},{"name":"Dagster","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Embodied AI","optional":false},{"name":"Fivetran","optional":false},{"name":"Google BigQuery","optional":false},{"name":"gRPC","optional":false},{"name":"Java","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"ROS","optional":false},{"name":"ROS2","optional":false},{"name":"Scala","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Time Series Forecasting","optional":false}],"status":"live","first_seen_at":"2026-07-10T23:57:45Z","employer_posted_date":"2026-07-10","last_verified_at":"2026-10-20T21:36:07Z","board_verified":true,"closed_at":null,"days_open":101,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":101},"description":"Location: Irvine, CA\nField AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven(1) approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.\nAbout Field AI\nField AI is at the forefront of robotic embodied AI, transforming industries like construction, security, mining, and manufacturing. Our autonomous robots operate globally, often in harsh environments, delivering critical insights to customers. Whether monitoring construction progress, ensuring safety compliance, or conducting predictive maintenance, Field AI is advancing technology to make a meaningful impact.\nLearn more at https://fieldai.com.\nAbout the Data Platform Team\nEvery robot we deploy generates a continuous stream of telemetry, sensor logs, and operational data from environments around the world. The Data Platform team builds the systems that capture every autonomy intervention, anomaly, and operational signal from globally deployed robots, classify them, and turn them into the ranked problem list that drives our engineering roadmap.\nAbout the Job\nAs a Data Platform Engineer, Data Pipelines, you will design and build the systems and integrations that help move data reliably from robots in the field to the teams and services that depend on it - analytics, autonomy, ML training, and deployment operations. You will collaborate with cross-functional teams spanning robotics, autonomy, and deployment to build the data backbone of a field robotics company.\nWhat You'll Get To Do\nDesign and build the data platform, frameworks, and developer tooling that power ingestion across Field AI.\nHandle the realities of field data: intermittent connectivity, large sensor payloads (LiDAR, camera, IMU), edge-to-cloud synchronization, and backfill from offline deployments.\nDevelop reusable ingestion SDKs, APIs, and services that enable teams to onboard new robotics data sources with minimal custom code.\nBuild and maintain integrations across heterogeneous sources: robot/edge systems, fleet management and deployment tooling, simulation outputs, and cloud object storage.\nIntegrate the platform with downstream consumers: BI tools, ML training and evaluation pipelines, labeling systems, and issue tracking.\nDevelop connectors and APIs (REST/gRPC, webhooks, CDC) so internal teams can feed data in and consume curated datasets reliably.\nOwn integration reliability end to end: schema contracts, versioning, retries, backfills, and monitoring.\nOptimize pipeline performance, scalability, and cost across growing fleet deployments.\nWhat You Have\nBachelor's or Master's degree in Computer Science, Engineering, or a related technical field.\n3-5+ years of experience in data engineering or backend engineering focused on pipelines and infrastructure.\nStrong programming skills in Python and SQL (C++, Scala, or Java a plus).\nProduction experience with streaming systems (Kafka, Kinesis, Pub/Sub) and orchestration tools such as Airflow or Dagster.\nExperience with a modern warehouse or lakehouse (BigQuery, Snowflake, Databricks, Redshift) and cloud object storage at scale.\nExperience building integrations across systems: third-party APIs, internal services, and CDC/ELT tooling (Fivetran, Airbyte, Debezium, or custom connectors).\nExperience building for data quality: testing, monitoring, lineage, and incident response.\nStrong problem-solving skills and ability to work in interdisciplinary teams.\nThe Extras That Set You Apart\nExperience with robotics, autonomy, automotive, or other telemetry-heavy operational data (bag files, fleet logs, time-series sensor data).\nFamiliarity with robotics middleware and log formats such as ROS/ROS2, MCAP, or rosbag.\nExperience with edge computing or intermittently connected data collection.\nExperience with dbt or similar transformation frameworks.","description_format":"text","description_chars":4136,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Robotics AI","Industrial 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