Senior Data Engineer

We’re hiring a Senior Data Engineer to own the lifecycle of demo data across all Showcase environments. From grooming datasets to scripting resets and mocking AI-generated scenarios, you’ll make sure our demo stories are always demo-ready — fast, relevant, and reliable.

As the dedicated Senior Data Engineer in Showcase, you’ll play a key role in ensuring our environments run like clockwork. You’ll prepare, maintain, and manipulate data to support high-impact demos and POCs.

Your work will sit at the intersection of engineering, storytelling, and data science. You'll work with the standard ModelBank data ingestion pipelines, but also go beyond — scripting custom triggers, building AI-driven data generators, and ensuring we can reset and rerun scenarios seamlessly. This is a highly autonomous, technical role, and you’ll collaborate closely with backend engineers, QA, and solutions teams to deliver real value through realistic, resilient data.

• Groom and maintain the Showcase data environment using the ModelBank ingestion process and custom scripts;
• Develop data manipulation tools and scripts to support creation, cleanup, and reset of demo scenarios;
• Leverage AI/ML models to synthesize demo data and enhance realism in customer journeys;
• Enable demo teams to trigger dynamic user stories through scripted workflows and scenario generation;
• Work closely with QA and backend engineers to ensure environments are stable, reusable, and scalable;
• Support DevOps practices including containerization, CI/CD integration, and cloud environment management.

You’re a hands-on data expert who loves solving real-world problems with smart, scalable solutions. You’re fluent in Python, comfortable with AI/ML, and you know your way around infrastructure just as well as databases. You thrive when your work unlocks speed, flexibility, and wow-factor experiences.

• 4+ years of experience in data engineering, scripting, or infrastructure-heavy data roles;
• Strong Python skills, including experience with pandas, NumPy, and scripting automation;
• Experience building or using machine learning models for data generation, augmentation, or classification;
• Experience with SQL and NoSQL databases, data transformations, and ETL workflows;
• Proficient in containerization (Docker), cloud platforms (Azure), and CI/CD tooling (e.g., Jenkins, GitHub Actions);
• Comfortable building, resetting, and maintaining clean environments to support frequent demo use;
• Bonus: Experience with mocking frameworks, synthetic data tools, or AI-based simulation techniques.