Pivotal is seeking a Data Engineer to build and operate the flight data warehouse — the system of record for telemetry off every aircraft in the fleet. Flight logs land continuously from customer and test aircraft; you will turn them into curated datasets, automated fleet health alerts, and dashboards that the firmware, GNC, battery, flight operations, and MRO teams rely on to make airworthiness and design decisions. This is a small team building production infrastructure, so you will own pipelines end to end: the data model, the services that run on it, the infrastructure as code that deploys them, and the monitoring that tells us when they break.
Responsibilities
- Pipeline development: Build and maintain the ingest and transformation pipelines that land aircraft telemetry from S3 into the flight data warehouse (AWS Glue, Athena), including schema evolution as firmware logging changes.
- Data modeling: Design and maintain curated marts — flight runs, component flight hours, vehicle modes, battery flight features — in SQL, with clear grain, documented lineage, and idempotent incremental refresh.
- Fleet alerting services: Develop and operate the Python services that evaluate flight-by-flight health checks and deliver alerts to engineering and customer-facing channels via SNS and Slack.
- Data quality: Own the automated quality gates — mart freshness, source-to-mart reconciliation, and watermark integrity — and block deploys that would publish incorrect fleet data.
- Analytics enablement: Build the dashboards and query interfaces engineering teams use to monitor the fleet, and convert repeated one-off analyses into supported datasets.
- Collaboration: Work directly with firmware, GNC, battery, systems engineering, flight operations, and MRO to define what gets logged, what gets alerted on, and what “good” looks like for each signal.
Qualifications
- Bachelor’s degree in Computer Science, Computer/Electrical Engineering, or a related technical discipline
- 3+ years of professional data engineering experience building and operating production pipelines
- Strong SQL, including window functions and query tuning on a distributed, columnar engine (Athena/Trino/Presto, Spark, Snowflake, or BigQuery)
- Proficient in Python for production services — not just notebooks: packaging, dependency management, unit tests, and code review
- Hands-on experience with the AWS data stack: S3, Glue, Athena, Lambda, SNS, CloudWatch, and IAM
- Dimensional modeling and warehouse design, including partitioning strategy for large tables
- Excellent analytical and written communication skills, with the ability to work across multidisciplinary teams
Preferred Qualifications
- 5+ years of relevant data engineering experience, including ownership of an on-call or operationally critical data system
- Infrastructure as code with Terraform, and building deployment pipelines in GitLab CI or equivalent
- Transformation frameworks such as dbt, and data quality/testing frameworks
- High-volume time-series or sensor telemetry, and observability tooling (Grafana, CloudWatch, or similar), including alert design that avoids alarm fatigue
Familiarity/experience with one or more of the following
- Aerospace, automotive, or robotics telemetry and flight/vehicle test data
- Embedded logging formats and decoding raw device logs (CAN, serial, or proprietary binary packet formats)
- FAA aircraft certification or continued airworthiness processes
- Interest in RC planes, quadcopters, or aviation
Similar Jobs
What you need to know about the San Francisco Tech Scene
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine

.png)
