Full Stack Data Engineer - Internal Flight Data Applications

Pivotal•Palo Alto, CA

9d•Onsite

About The Position

Pivotal is seeking a Full Stack Data Engineer to build the internal applications that put flight data directly in engineers' hands, so teams across the company can explore it and answer their own questions. You will work across the whole stack — backend services over the data platform, web front ends that make large datasets explorable, dashboards, AI-assisted tooling that helps engineers interpret what they are looking at, and the packaging that puts analysis tools on an engineer's laptop. Your users sit down the hall, and requirements arrive as a conversation with someone who needs an answer rather than as a written spec, so this role is as much about understanding the question as it is about shipping the tool. It suits an engineer early in their career who wants ownership of real products and direct contact with the people building and flying the aircraft.

Requirements

2+ years of professional experience building and shipping web applications, backend through front end
Bachelor’s degree in Computer Science or a related technical discipline, or equivalent practical experience
Proficient in Python, and comfortable writing SQL against a real database
Experience with a modern JavaScript or TypeScript front-end framework (React, Vue, or similar)
Has built and consumed HTTP APIs, including authentication, pagination, and error handling
Comfortable with Git-based workflows, code review, and CI/CD
Can sit with a non-software engineer, work out what they actually need, and ship it — most requirements here arrive as a conversation, not a spec

Nice To Haves

AWS experience, particularly S3, Athena, Lambda, or API Gateway
Worked with large or time-series datasets, and knows how to keep a UI responsive over millions of rows
Charting and data visualization libraries; Grafana dashboard development
Docker, and infrastructure as code with Terraform
Has built with LLM APIs or agent frameworks — tool use, retrieval over structured data, and evaluating whether the output can be trusted
Has built internal tools where the users are colleagues rather than customers
Aerospace, automotive, or robotics telemetry, and flight or vehicle test data
Packaging desktop or command-line tooling for non-developer users, including on Windows
Authentication and access control for internal applications (SSO, IAM, scoped credentials)
Regulated or safety-critical environments with traceability and data retention requirements
Natural-language or conversational interfaces over structured, time-series, or diagnostic data
Interest in RC planes, quadcopters, or aviation

Responsibilities

Build and maintain the internal web applications engineers use to work with flight data — flight record browsers, query interfaces, and analysis tools over the warehouse.
Develop Python services and HTTP APIs that query Athena and S3 and return results at interactive speed, including pagination and caching over large result sets.
Build interfaces that make large telemetry datasets explorable — sortable tables, filters, and time-series plots — for engineers who are never going to write SQL.
Build agent-based tooling over the data platform so engineers can ask questions in plain language and follow a symptom through to a root cause without hand-writing queries. Design these tools to return the underlying data and how it was derived, not just an answer, so an engineer can verify the result before acting on it.
Turn recurring one-off analyses from the firmware, GNC, battery, and maintenance teams into supported, self-serve tools instead of scripts that only their author can run.
Generate the internal field guide and data catalog from the warehouse itself, so what engineers read cannot drift from what the tables actually contain — and keep that metadata machine-readable, so the tooling and agents built on top of it interpret results correctly.
Build the review and triage surfaces for fleet health alerts — acknowledgment, history, and trend views — alongside the existing dashboards and Slack notifications.
Package and distribute analysis tooling so it runs on an engineer's laptop, Windows included, without someone walking them through an environment setup.
Carry your work through deployment — tests, code review, CI/CD, and the monitoring that tells you when something breaks.
Sit with the engineering teams to scope what they actually need, and document what you ship so the next person can support it.