Data Engineer (Tulsa)

Job description

Ten-Nine creates new materials for new economies. Our proprietary nano-additive, TENIX, dramatically improves the function of cathodes - the primary driver of battery cost, performance, and greenhouse gas emissions. Its a simple powder, produced sustainably and domestically, that can be integrated into manufacturing lines for all kinds of batteries, from single-use primary cells to rechargeable EV packs.

Role Overview

The Data Engineer will help build and maintain data infrastructure, analyze battery test and operational data, and work closely with R&D, manufacturing, and quality teams to drive improvements in battery optimization, processes, and performance. This role combines data-driven and AI methods with engineering expertise to model, predict, and optimize battery behavior under real-world conditions.

Key Responsibilities

Design, build, and maintain data pipelines and data stores (test data, manufacturing data, sensor data, etc.).

Clean, preprocess, and validate large datasets from battery cycling tests, diagnostics, environmental tests, and field usage.

Develop machine learning and statistical models to predict metrics such as state-of-charge (SoC), state-of-health (SoH), degradation, thermal behavior, and safety events.

Perform exploratory data analysis to identify patterns, anomalies, and correlations in battery performance.

Collaborate with battery engineers, electrochemists, and manufacturing staff to define data requirements and design experiments.

Implement monitoring tools, dashboards, and visualization to track battery health, production quality, and performance over time.

Establish guidelines and best practices for data collection, versioning, quality assurance, and metadata management for battery datasets.

Optimize data flows for computational efficiency and scalability, including high-frequency sensor data, large test datasets, and real-time streaming.

Integrate battery system simulations and connect with physics-based models for predictive analysis.

Day-to-Day Responsibilities

Handle data exports in CSV / Excel, small databases, and occasional cleaning tasks.

Manage manual uploads to LIMS, SharePoint, or similar systems.

Implement ETL processes (Extract, Transform, Load) for cycling, impedance, and degradation datasets.

Automate instrument data capture using APIs, DAQ, and related tools.

Architect and maintain pipelines from diverse data sources (lab instruments, cycling chambers, BMS logs, thermal sensors, MES / ERP systems).

Create and maintain dashboards for R&D and manufacturing performance monitoring.

Enforce metadata standards and ensure reproducibility of R&D experiments.

Conduct trend analysis, regression, and lightweight machine learning projects across multiple datasets.

Contribute to weekly / monthly reporting and collaborate with both R&D and manufacturing scale-up teams.

Support predictive modeling, process optimization, and integration with physics-based simulations.

Qualifications Required

Bachelors or Masters degree in Data Science, Computer Science, Electrical / Chemical / Materials Engineering, Physics, or related field.

Proficiency in programming (Python, SQL, related tools).

Experience with machine learning and statistical modeling (regression, time series, anomaly detection).

Experience handling experimental / test / multivariate data, including cleaning, preprocessing, and dealing with noise / missing data.

Ability to quickly learn battery systems : cell / module / pack architectures, test protocols (cycling, impedance, thermal), degradation mechanisms, etc.

Strong communication skills with the ability to translate technical results into actionable engineering insights.

Preferred

Experience in battery development (test labs, battery manufacturing, or R&D).

Knowledge of electrochemistry, thermal properties, and aging mechanisms of cells.

Experience with hardware and sensors embedded in battery systems (data acquisition, signal conditioning).

Familiarity with cloud platforms (AWS, Azure, GCP), streaming data, and big-data infrastructure.

Experience designing dashboards, monitoring systems, and real-time reporting solutions.

Prior work with simulation and modeling tools (battery simulation, thermal models, finite element).

Experience building automated data capture pipelines from instruments and test equipment.

Ability to manipulate and analyze large datasets at scale, applying programming expertise to data handling.

Strong focus on predictive modeling, degradation mechanisms, and optimization.

Seniority / Levels

This role is open to candidates at multiple levels of experience. Consideration will be given to both early-career and experienced professionals with the required skills and interest listed above.

Location

Tulsa, Oklahoma | Global Remote Consideration Available

Why Join Us

Be part of an innovative startup at the forefront of energy storage, combining materials science with data engineering.

Contribute directly to the development of sustainable, domestically produced energy solutions with global impact.

Work closely with world-class scientists, engineers, and industry leaders to shape the future of batteries.

Grow your career in a fast-paced environment where data-driven insights fuel breakthrough innovation.