Data Engineer II

About the role

This role supports Bid-to-Bill Operations by resolving production issues and automating manual workflows. The position improves system reliability and customer outcomes in energy market operations. It converts operational knowledge into software-driven, permanent solutions.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Production incidents and customer escalations receive timely resolution as a trusted technical resource. Recurring operational problems are solved by building automation that removes manual, repetitive steps. Monitoring, alerting, dashboards, and diagnostic tooling enhance system observability for production environments. Workflow and process automation reduce manual effort in market operations. Permanent improvements are implemented by converting production learnings to stabilize behavior and prevent recurring issues. Team resilience and continuity are strengthened by documenting operational knowledge and sharing it. Critical production issues are supported through after-hours availability as part of operational duties.

Requirements

A four-year college degree or equivalent working experience is required as a baseline qualification. At least 3 experience with C#, JavaScript, and SQL is required. Strong skills in SQL and MS SQL Server for data modeling, querying, and optimization are required. Experience with the Microsoft .NET ecosystem and relevant data technologies in production is required. Clear written and verbal communication across teams and with clients is required.

Nice to have

Knowledge of power trading, settlements, billing, and energy transactions in North American markets is valued. Familiarity with Microsoft Azure for cloud services and infrastructure operations is preferred. Experience with Vue.js for front-end interactions in operations tools is preferred.

Practical notes

This role may involve up to 5% travel. Hybrid schedule includes 2-3 days in the office as needed by the team. Reporting is directed to an Operations Engineer Manager within the Bid-to-Bill Operations team.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

The role emphasizes production reliability, automation, and customer outcomes in energy market operations. AI-assisted development and operational tooling accelerate investigation, coding, documentation, testing, and workflow improvement. Understanding general market, power trading, and settlement concepts helps contextualize operational workflows. Tools commonly used in this field include C#, SQL Server, observability platforms, and automation frameworks. Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.