Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global technology and engineering organization is expanding its infrastructure planning and construction capabilities through advanced data engineering, machine learning, forecasting, and optimization technologies. Its Machine Learning Platform enables engineering and facilities teams to design, build, and manage large-scale infrastructure more efficiently by turning complex operational data into actionable insights.
The organization is seeking a Data Engineer, DesignX to play a critical role in building and scaling the data foundation that powers its Machine Learning Platform. The role will focus on developing robust data pipelines and models that source, transform, and serve large-scale datasets enriched with real-world field context.
The position will contribute to delivering trustworthy, high-resolution data at scale while supporting neural networks, forecasting, and optimization systems. The successful candidate will help drive scalability improvements, develop new capabilities, and expand data and modeling infrastructure across multiple sites and regions.
What You’ll Do
- Design, build, and maintain end-to-end data pipelines that unify data from disparate sources into a single, trusted operational data model.
- Develop and operate high-volume, real-time, and batch data platforms for high-resolution time-series data using Python, modern orchestration, and streaming technologies.
- Architect robust databases and storage systems for infrastructure, equipment, power, and energy data.
- Build reconciliation, validation, and monitoring systems that ensure data quality, accuracy, and freshness across the platform.
- Build, train, and deploy time-series forecasting, optimization, and anomaly-detection models for power demand, energy consumption, cost, and capacity.
- Develop backend APIs using technologies such as FastAPI, Flask, or Django to serve structured data and model outputs to dashboards, applications, and other platform tools.
- Deliver interactive dashboards and tooling that transform complex power, energy, and cost data into actionable insights for design engineers, facilities teams, and leadership.
- Scale data collection and pipelines across multiple sites and regions, including automated sensor synchronization and scheduled computations supporting real-time forecasting and optimization.
- Partner with software and machine learning engineers to translate operational requirements into data architecture and modeling decisions.
- Define and improve engineering standards to ensure production-grade data platforms and applications.
What You’ll Bring
- A degree in computer science or an equivalent field, with 3+ years of professional experience as a data engineer or backend engineer building large-scale data platforms.
- Advanced proficiency in Python and SQL, including experience developing performant queries, data models, and pipelines at scale.
- Experience with databases and data platforms such as PostgreSQL, MySQL, SQL Server, and data lakes.
- Experience across the full data lifecycle, including sourcing, ingestion, transformation, analytics, and visualization, with the ability to own projects end-to-end.
- Hands-on experience with data processing and orchestration frameworks such as Spark, Airflow, dbt, or Dagster in production environments.
- Production experience with real-time and distributed streaming systems such as Kafka, Spark Streaming, or Flink.
- Proven experience building and deploying time-series forecasting models using high-resolution operational data.
- Hands-on experience formulating and solving mathematical optimization problems, including mixed-integer linear programming and convex or non-convex optimization.
- Experience with optimization technologies such as PuLP, Pyomo, Gurobi, or CVXPY.
- Experience integrating and reconciling data across multiple source systems with conflicting schemas or update cadences.
- Experience developing monitoring solutions for data quality, accuracy, and freshness.
- Proficiency with data visualization technologies such as D3.js, Tableau, Streamlit, or React.
- Excellent interpersonal, communication, and cross-functional collaboration skills.
Disclaimer
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- No Client Relationship: This company is not a client of SpringCube unless stated.
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