Senior Data Engineer

Overview

We’re looking for a hands-on Senior Data Engineer who thrives on building. This role goes beyond designing architecture—we need someone who’s excited to dig into real-world data problems, engineer robust pipelines, and optimize how data flows across systems. You’ll work at the core of our AI and analytics strategy, helping build the data foundation that powers our Measurement & Verification (M&V) tools, product analytics, and smart building optimization efforts.

From scraping utility websites to feeding machine learning models and operational dashboards, you’ll be at the heart of our emerging Data Lake and MCP platform. If you’re energized by solving complex data engineering challenges, love connecting systems, and want your work to have real, measurable impact—you’ll fit right in.

Pay: $130,000 - $160,000

Location:
Hybrid – Dallas, TX

• Engineer production-grade data pipelines (batch & real-time) that move and transform data between diverse internal and external systems
• Build custom ingestion solutions, including web scrapers, scripted loaders, and automation routines to retrieve utility, weather, and building systems data
• Design and maintain integrations with internal APIs, third-party sources, and cloud storage
• Partner with Data Scientists, Product Managers, M&V Analysts, and Operations leadership to make data useful, actionable, and accessible across teams
• Develop scalable ETL/ELT pipelines that support a wide variety of downstream use cases, including AI model training, alerting, reporting, and more
• Support the ongoing development of our AI-ready Data Lake, ensuring high quality and integrity of ingested data
• Drive performance, reliability, and observability into our pipelines using tools like Airflow, dbt, and AWS-native services
• Maintain a strong builder mindset—you’ll be writing code more than drawing diagrams

Must-Haves

• 5+ years of experience building data pipelines and automation tools in production environments
• Deep fluency in Python, SQL, and data manipulation libraries (e.g. pandas, pyarrow)
• Proficient in data modeling, version control, containerized workflows (Docker), and orchestration tools like Airflow
• Comfortable working across cloud-native stacks, particularly AWS (S3, ECS, Lambda, Glue, Athena, Redshift)
• Strong grasp of APIs, data formats (JSON, XML), and integration patterns
• Experience with web scraping frameworks (e.g., Puppeteer, Playwright, or Beautiful Soup)
• Passion for delivering high-quality, production-ready code in collaborative team environments

Nice-to-Haves

• Experience working with energy data, utility billing, or IoT sensor feeds
• Familiarity with machine learning pipelines, time-series analytics, or anomaly detection
• Comfort with CI/CD pipelines, IaC (Terraform), and observability tools
• Background in Data Ops or experience setting up data quality and validation frameworks