Project Controls Data Analytics & AI Tools

Blue Origin; Merritt Island (FL); On-site; USD 120,000 - 180,000; Full time

Job summary

Blue Origin seeks a data/controls professional to own the controls data model and associated tools. You will build data pipelines, dashboards, forecasting and AI agents aligning with PE and leadership needs.

You will work across PLM, ERP, MRP, scheduling and analytics to provide trusted data for decision making. You will design and ship tools that PEs use, with guardrails, version control, and measurable improvements in efficiency and accuracy.

Qualifications

• Bachelor's degree in a quantitative or technical field or equivalent experience.
• 5+ years in project controls, business intelligence or data analytics with 2+ years building data products.
• Experience turning business problems into technical requirements and shipping dashboards.
• Strong SQL and Python; experience building production data pipelines (ETL/ELT).
• Data modeling (star schemas, snapshots, slowly changing dimensions).
• Git version control experience.
• Ability to explain data and model limits to engineers and leaders.
• U.S. citizenship or eligible status (citizen, permanent resident, asylum/refugee status).

Responsibilities

• Define architecture requirements for the controls toolchain across PLM, ERP, MRP and analytics.
• Map data sources and document data flows between systems.
• Test tools against requirements and maintain version control.
• Link design releases and engineering changes to enable end-to-end traceability.

Skills

SQL

Python

Data pipelines

Power BI

Tableau

Git

LLMs/AI tooling

EVM metrics

Education

Bachelor's degree in a quantitative or technical field

Master's degree preferred

Tools

Databricks

Spark

AWS

Azure

Primavera P6

MS Project

EVMS

Job description

At Blue Origin, we envision millions of people living and working in space for the benefit of Earth. We're working to develop reusable, safe, and low-cost space vehicles and systems within a culture of safety, collaboration, and inclusion. Join our team of problem solvers as we add new chapters to the history of spaceflight!

This role is part of Blue Origin Operations, which is comprised of Integrated Supply Chain, Test Operations, Safety, Quality, and Mission Assurance. This includes Manufacturing and Supply Chain support across all Blue Origin facilities.

As part of a hardworking team of specialists, technicians, and engineers you will connect the systems that hold the project's truth: schedules, cost and labor actuals, procurement, change requests, engineering configuration and risk. You will turn them into one trusted controls dataset. On top of it you will build the dashboards, forecasts and AI agents that remove manual status work. Success means each PE carries more projects, with earlier warning of slips and overruns, and leadership sees the portfolio without asking anyone for a slide.

This is a builder role. You will own the controls data model and the tools built on it: data pipelines, dashboards, forecasting and risk models, and AI agents. You don't need an engineering background. You do need to learn the engineering data (design releases, BOMs, engineering changes) well enough to model it, working alongside PEs and technical leads. You will ship working tools that PEs use.

Responsibilities include but is not limited to:

Requirements and architecture of the controls toolchain

• Gather PE, finance and leadership needs and turn them into technical requirements for each data product and tool.
• Map which system is the source of truth for each data element across PLM (Windchill), ERP and MRP, change tools (Jira, ITS, Change Orchestrator), scheduling (P6) and the analytics platform. Document the data flows between them.
• Test each tool against its requirements before release. Keep code, data models and AI agents under version control.
• Link design releases, engineering changes, schedule activities, cost and risk so each project can be traced through its gate reviews (SRR, IDR, FDR, Pre-Activation Review).

Forecasting, risk and simulation models

• Build and run Monte Carlo schedule and cost risk models on resource-loaded schedules, to set confidence levels for gate reviews and baselines.
• Estimate the schedule, cost and resource impact of proposed changes, so the PE can make the call with data.
• Build estimating models from completed GSD projects for hours, cost and duration.

Controls data foundation

• Build and maintain data pipelines that join schedule (Primavera P6 / IMS), cost and labor actuals, procurement, change requests, engineering configuration (PLM) and the risk register into a governed controls dataset, mostly in Databricks.
• Define the controls data model: project, WBS, control account, work package, change, risk. Own its data dictionary and quality checks.
• Find and fix data-quality gaps at the source. Missing baselines, unmapped charge codes and stale schedule status are common examples.

Analytics and reporting

• Build and run portfolio and project dashboards: schedule health (DCMA-style checks), cost and EVM metrics (CPI, SPI, EAC/VAC), change throughput, risk burn-down and PE workload.
• Automate the weekly status and variance review and the monthly forecast package, so PEs review and approve instead of compiling.
• Build forecasting models for schedule slip, cost at completion and resource demand. Use history from completed projects to calibrate them.
• Model PE and IPT capacity against intake demand, to support accept, defer or decline decisions at intake.

AI tools and automation

• Design, build and deploy AI agents and LLM-based tools for controls work. Examples: project setup across systems, schedule-to-scope consistency checks, variance narratives, change impact summaries, risk identification from engineering and test records.
• Put guardrails around every tool: human approval on anything that changes a baseline, logged sources for every generated number, and tests that catch regressions.
• Measure each tool's effect in PE hours saved or cycle time cut, and retire tools that don't earn their keep.
• Work within ITAR and data-handling rules. Use only approved internal AI platforms and data boundaries.

Adoption and standards

• Work directly with PEs, Systems Engineering, finance, procurement and site teams to find the highest-value manual work and automate it first.
• Write the standards, runbooks and training that let PEs use the tools without help.
• Partner with enterprise IT, data and tool teams so GSD's tools build on company platforms instead of duplicating them.

Minimum Qualifications

• Bachelor's degree in a quantitative or technical field, such as data science, statistics, information systems, computer science, mathematics, economics, finance, business analytics, construction management or engineering (or equivalent combination of education and directly relevant experience).
• 5+ years in project controls, business intelligence or data analytics, with at least 2 years building data products or tools for project, construction, engineering or finance teams.
• Experience turning business problems into technical requirements, and shipping a dashboard or data product that a team used to make decisions on a regular cadence.
• Strong SQL and Python. Experience building production data pipelines (ETL/ELT), not one-off analyses.
• Data modeling experience (star schemas, snapshots, slowly changing dimensions) and a BI tool such as Power BI or Tableau.
• Working knowledge of project controls: CPM scheduling, baselines and change control, cost and EVM metrics.
• Hands-on experience building with LLMs or AI tooling: prompting, retrieval, tool or agent workflows, and evaluating their output.
• Experience with version control (Git).
• Ability to explain data and model limits plainly to engineers and leaders.
• Must be a U.S. citizen or national, U.S. permanent resident (current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

Preferred Qualifications

• Master's degree in analytics, data science, statistics or a related field.
• Experience with Databricks or Spark, and with cloud data platforms (AWS or Azure).
• Experience with Primavera P6 or MS Project data structures, and with EVMS (EIA-748) reporting.