Senior Software Engineer

Join us as a Sr. Software Engineer on the R&D Software Team

and own real software from the ground up — not a ticket queue. You'll shape the roadmap, make the technical calls, and build the real-time data platform that turns industrial IoT telemetry into decisions our customers can act on.

Where You'll Work:

This is a remote-friendly role — most of your work in practice can be done remotely, with occasional in-person collaboration at our Porto office. Our engineers tell us that flexibility is one of the things they value most about working here.

Job Duties and Responsibilities:

Reporting to the Digital Integration Director, you will own software solutions end to end, from framing the business problem to running services reliably in production, built on real-time telemetry from industrial IoT sensors monitoring liquid and gas storage tanks across industries including industrial chemical, industrial gas, propane, and other fuels.

Collaborate for success

• Own projects end to end, starting from a greenfield state: define your own requirements, shape the roadmap, and take solutions through to production.
• Work cross-functionally with teams like Sales and Marketing, translating business goals into working software.
• Make the technical decisions and contribute significantly to the implementation — this is a hands-on role, not a hand-off-to-others one.

Build data-driven systems

• Build and operate the backend services and APIs behind a platform ingesting real-time telemetry from industrial IoT sensors at sites across the globe.
• Design and run the data pipelines that turn that telemetry into something people can act on — including forecasting and anomaly detection on time series.
• Work with the realities of sensor data: gaps, drift, irregular sampling, and a device population that keeps evolving. Bring the statistical judgment to tell a real signal from noise, and to say how confident we should be.
• Run what you build — instrumentation, alerting, and the operational habits that keep a system healthy.

Engineer with AI assistance

• Use agentic coding tools — Claude Code, Copilot, Cursor, and similar — as a normal part of daily delivery.
• Hold AI-generated code to the same bar as any other code. You are accountable for what you ship.
• Structure repositories, tests, and documentation so both people and agents can work in them effectively, and share the patterns and guardrails that work so the team's baseline rises.
• Apply Anova's AI Handbook guidance on risk and human-in-the-loop validation to any system whose output reaches a customer or drives an automated action.

Advocate for quality

• Contribute to and continuously adapt best practices and Ways of Working across software engineering, data engineering, and platform operations, so the team ships high-quality solutions that create real impact for our clients.

What Success Looks Like:

• Backend services and data pipelines for real-time IoT telemetry run reliably in production, with strong instrumentation and alerting in place.
• Forecasting and anomaly detection outputs are trustworthy enough that customers and internal stakeholders act on them with confidence.
• Engineering practices — code review, testing, CI/CD, AI-assisted development — visibly raise the team's baseline, not just this person's own output.
• Other engineers are actively mentored and leveled up through code review and design discussion.
• Business stakeholders trust this person to translate technical trade-offs and uncertainty into decisions they can act on.

What you'll need to succeed:

Minimum Requirements -

• Bachelor's degree in Computer Science, Engineering, Physics, Mathematics, or another quantitative or scientific field — or an equivalent combination of education and experience.
• 5+ years in software engineering or a closely related role, including hands-on production deployment (6–8 years preferred). A completed PhD in a quantitative or scientific discipline, with substantial hands‑on programming, counts toward this.
• Strong Python and the engineering habits that go with it: git, code review, linters, unit tests, and CI/CD pipelines are things you use daily.
• Strong analytical ability and working fluency with statistics: distributions, uncertainty, correlation and causation, and knowing when a result is real.
• Solid SQL and experience working with data at scale — pandas, PySpark, and platforms such as Databricks, Snowflake, or Postgres.
• Experience operating software in production: deployment, monitoring, alerting, and a feel for the practices that make that sustainable.
• Fluency with agentic coding tools.
• Fluent i