Analytics 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.

The Position

OPSWAT's data platform turns CRM and business-system data into governed, documented, tested tables that business teams can read without asking what a column means. It is built on dbt Core, layered source → staging → intermediate → mart, and it is migrating on two fronts: a large estate of legacy stored procedures into that model, one domain at a time, with value parity proven before anything is cut over and the warehouse platform itself, from Microsoft Fabric Warehouse to Azure PostgreSQL, potentially an Elastic Cluster. You will work across both engines while that move completes.

As a member of the Data Engineering team, you will own the modelling for the domains assigned to you, and the programme's cross-cutting craft: the dbt layer standards and the tooling that enforces them. This is a hands-on modelling role for someone who wants their name on the rules, not just on the tickets. You will decide what "correct" looks like for a model, prove it with numbers, and make the standard checkable by script rather than by memory. You will work in an AI-assisted engineering workflow, where agentic tooling accelerates the work but the modelling decisions: grain, layer, parity.

You will work as one half of a deliberate pairing: the Data Engineering team on the technical side, Product Analysts embedded in the product pods, one per business domain on the business side. Product Analysts own what the domain means, the certified metrics and their definitions, and whether the result is right in business terms. Analytics Engineering owns the technical spectrum: modelling, layers, grain, keys, tests, parity, and the standards and tooling the models are built to. Both halves write SQL and dbt; they bring the domain, you carry the technical bar, and the two halves answer for the same number.

What You Will Be Doing

- Model the business domains you are assigned in dbt Core from their source tables through layered staging and intermediate models to tested marts; working across Microsoft Fabric and Azure PostgreSQL as the platform migrates.

- Work as the technical counterpart to the Product Analysts who own each business domain: they bring the business definition and the questions the data must answer; you make the models correct, tested, and proven by parity.

- Build the tested marts that certified metrics sit on, so a Product Analyst's definition and the model beneath it never drift apart.

- Prove value parity between each dbt mart and the legacy stored procedure it replaces: row counts and key aggregates recorded, not asserted, before any consumer is repointed.