Analytics Engineer

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 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 the warehouse is moving from Microsoft Fabric Warehouse to Azure Database for PostgreSQL. You will learn the craft on the real thing: models that ship, with value parity proven before anything is cut over.

This is the entry point to analytics engineering on this team, and it is a working role rather than an observation one. You will build models for the domains assigned to you and they will go into production. What differs from a more senior role is the supervision, not the bar: every change is reviewed before it lands, you have a named mentor, and you are paired with a Product Analyst on the business side: the two halves answer for the same number.

By day 60 you are expected to be delivering a model with less correction than your first one, not to still be waiting for instructions.

What You Will Be Doing

• Build staging, intermediate and mart dbt models for the domains assigned to you, following the team's layer standards: what belongs in each layer, where collation is normalised, one path in per source.

• Write tests that fail when the data breaks, not checks that pass whatever the data does.

• Run parity comparisons between a dbt model and the legacy procedure it replaces: execute the comparison, record the numbers, and learn what "proven" means before a consumer is repointed.

• Investigate the quality failures your tests surface, and trace them back to the source instead of patching the symptom in the model.

• Keep documentation and lineage current as models change, so the next reader does not have to ask what a column means.

• Work across both engines while the warehouse migrates, and notice when the same model behaves differently on each.

• Work in an AI-assisted engineering workflow. You set the task, review the output, and own what lands.

• Pair with a Product Analyst to learn what the business needs from a number, and why a definition is worth arguing about.

• Ask for review early, and act on it.

What we need from you

• 0–2 years in analytics engineering, data engineering, or a comparable role or a portfolio that shows the same work.

• Working SQL: joins, filters, aggregations, CTEs. Window functions are the next thing you learn, and you are already reading about them.

• Some Python for data work. A script you wrote, a notebook you finished, a pipeline you broke and then fixed.

• Git and pull-request basics, or the willingness to learn them in the first week.

• The habit of checking your own numbers before sharing them: reconciliation, sanity checks, and saying so when something does not add up.

• Curiosity about how the business actually works. You want to know why the data looks the way it does.

• Willingness to ask for help early. On this team that is the norm, not a weakness.

It Would Be Great If You Also Have

• Exposure to dbt, or another transformation framework, from coursework or a project.

• A BI tool you have built in: Superset, Power BI, Looker, or similar.

• A data project you can talk about for ten minutes: what it was for, what you got wrong, and what you changed.

• Comfort in a terminal: Docker, a CLI, a cloud console.

• Azure basics: storage, identity, or a service you have actually used

What success looks like in your first 60 days

• Your first models are merged, reviewed, and pass the layer-standards check. You learned the rules by applying them.

• A test you wrote catches something real, or would fail if the model broke.

• You have run a parity comparison end to end and can explain what "proven" means without reading the definition back.

• You can explain your domain's data in business terms, and why the definition matters.

• Your reviewer's comments are getting fewer and smaller: the next model needs less correction than the first.

OPSWAT is an equal opportunity employer. We celebrate diversity and are committed to providing an environment where equal employment opportunities are extended to all employees and applicants, free of discrimination and harassment of any type. All employment decisions are based on individual qualifications, job requirements, and business needs without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other category protected by federal, state, or local laws.

Recruiting Agencies: we do not accept unsolicited resumes from third party agencies for any of our open positions. To submit resumes for our jobs, there must be a recruiting contract approved by our legal team and endorsed by both parties. We are currently not accepting additional 3rd party agencies at this time.