Senior Data Engineer

About the role

OPSWAT, a global leader in IT, OT, and ICS critical infrastructure cybersecurity, delivers an end-to-end platform that gives public and private sector organizations and enterprises the critical advantage needed to protect their complex networks, secure their devices, and ensure compliance. Over the last 20 years our commitment to innovative technology has earned the trust of more than 1,700 organizations, governments, and institutions globally, solidifying our role in protecting the world’s critical infrastructure and securing our way of life.

About the Role

We are looking for a Senior Data Engineer who combines deep business understanding with analytical expertise to shape the internal products we build. As AI-powered analytics enables business teams to create their own reports and dashboards, this role focuses on defining and governing trusted metrics, working with data teams to ensure their reliability, and identifying opportunities to improve business workflows. Embedded within a product team, you will translate those insights into requirements and help develop data and analytical features that support better decisions and actions within the product itself.

You will be assigned to Sales & Marketing domain and work alongside the product pod that serves it, with the data platform and modern AI tooling to build on.

Key Responsibilities

- Metric ownership: Own the definitions, documentation, and change control for your domain’s certified metrics. Maintain a single source of truth so that teams asking the same question get the same number, and act as the point of resolution when definitions are challenged or diverge.

- Data quality: Define and enforce freshness, completeness, and accuracy expectations for the datasets and pipelines behind your domain’s metrics. Build automated dbt tests and monitoring into the pipelines you own, investigate failures and anomalies, trace root cause back to source, and escalate upstream issues to the Data Platform team with supporting evidence.

- Analytics engineering (dbt): Design, build, and maintain the dbt models and semantic layer that serve your domain — written in SQL and Python, version-controlled in Git, tested, and documented. Follow the team’s modelling standards for layering, naming, and incremental logic, and keep dbt documentation and lineage current as models change.

- In-product features: Build in-product data and insight features end to end: embedded metric panels and insight cards, exception and alert surfaces, prioritized work queues, agent-assisted workflow steps, and the instrumentation behind them. Prototype in a sandbox, then ship through engineering review, tests, and feature flags alongside the pod’s engineers.

- Analysis & reporting: provide support to business users with more complex analytics requests, with the goal to enable them to self-service in the long run.

- Collaboration: Partner with product managers, engineers, and business owners in your domain; document your work so others can use and extend it; and support business users querying certified metrics themselves.