Senior Data & Insights Engineer

About Strategus

Strategus is a CTV‑first, fully managed service partner and pioneer in programmatic Connected TV-focused on defining "What's Next" in data‑driven advertising. Our culture is built on three core values-Respect, Full Engagement, and Growth-which show up in how we collaborate, make decisions, and show up for our clients and each other every day.

Our north star is simple: to make Strategus the best job you've ever had. Here, you'll join a high‑performing team that invests in your development, values your voice, and gives you the opportunity to shape what's coming next for CTV and the brands we serve.

As Data Manager / Engineer at Strategus, you'll own day-to-day operation of our Snowflake warehouse, the Sigma semantic layer that sits on top of it, and the pipelines that feed both. This is a senior individual-contributor role reporting to our Director of Tech and Product, hands-on with SQL, modeling, and integration. You'll partner with our managed data partner (Datateer), Sigma and its native AI agents, our automation layer (Convey), and stakeholders across AdOps, Sales, Finance, and Executive.

• Snowflake: account admin, role and security configuration, performance and cost management alongside data leadership.

• Sigma semantic layer: build and maintain the governed datasets (joins, transpositions, derived metrics) that power AdOps, Sales, Finance, and Executive reporting and Sigma's native AI agents.

• dbt models: spec what's needed, scope with Datateer (who builds and runs them), review their work, keep the warehouse catalog current.

• Fivetran (in-house): operate Salesforce, HubSpot, and other ingestion that lives with us.

• Reverse-ETL into Salesforce, HubSpot, NetSuite: especially flows where the decision logic lives in Sigma. Partner with the named owner of each destination before any write goes out.

• Datateer service requests: triage, prioritize, sign off on scope, escalate when blocked.

• Sigma access governance: keep dataset curation tight; approve explore- and build-access changes alongside data leadership.

• Dashboards: internal and customer-facing views, with attention to reliability, performance, and clarity for non-technical users.

• Convey + Sigma agents: help shape where each tool lives in the stack, and design the Sigma Actions / Convey integration seam.

• End-of-month and financial-adjacent analytics: delivery-in-full and pacing frameworks, sales order vs. invoice reconciliation, media cost validation. Build repeatable processes that reduce manual work.

• Data quality SLAs for AdOps, billing, and Salesforce datasets; detect, prevent, and remediate at the source.

• Analysis and experiments: patterns in campaign performance, pricing, margin, and throughput; bidding, pacing, and audience tests with appropriate sizing and structure; recommendations to operating leaders.

• Automation, documentation, reproducible workflows to de-risk month-end and seasonal processes.

• Expert SQL and data warehousing: schema design, performance tuning, modeling for analytics workloads.

• Cloud data platforms: hands-on, ideally Snowflake (cost / performance, role-based security, workload management).

• Modern ELT / data engineering tooling: Fivetran or similar; dbt or dbt-style modeling; reverse-ETL (Census, Hightouch, Fivetran Activations).

• BI / data visualization: ideally Sigma; governed datasets, internal and customer-facing dashboards, narratives for business users.

• Salesforce + cross-system integration: GTM, revenue, and delivery datasets across Salesforce, ERP / invoicing, and DSP / platform data.

• Cloud-warehouse APIs: designing or consuming APIs for third-party platform integration.

• Working with managed data partners: scoping vendor / consultant work, reviewing output, holding the relationship to outcomes.

• Working alongside AI tooling: BI-native AI agents (Sigma) and cross-system automation (Convey), with a point of view on where each belongs.

• Programming: Python, R, Go, or similar for data processing, modeling, and automation.

• Analytical and statistical foundation: predictive modeling basics, A/B and multivariate experimental design, measurement and KPI design.

• Translating complex data into simple, actionable outputs for non-technical stakeholders.

• AdTech / programmatic experience (CTV, open web, DSPs such as The Trade Desk) a strong plus.

• Education: BS or MS (preferred) in CS, Data Science, Statistics, Math, or related quantitative field; or equivalent professional experience.

• 7+ Years experience in a data engineering or BI role, in a senior IC or lead capacity

Strategus is an equal-opportunity employer. We are committed to diversity and inclusion, as well as non-discrimination in employment. All qualified applicants receive consideration without regard to race, color, religion, gender, sexual orientation, national origin, age, veteran status, disability unrelated to performing the essential task of the job or other legally protected categories. If you're smart, scrappy and good at what you do, come as you are. We are committed to creating an inclusive environment.