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At Oscilar, we're building the most advanced AI Risk Decisioning™ Platform. Banks, fintechs, and digitally native organizations rely on us to manage their fraud, credit, and compliance risk with the power of AI. If you're passionate about solving complex problems and making the internet safer for everyone, this is your place.
As an Analytics Engineer, you will be a foundational member of Oscilar’s GTM Ops & Strategy team, helping build the data foundation for scalable analytics across the organization.
You will partner closely with stakeholders across Ops, Finance, and the GTM org, including Sales, Marketing, BDR, and Customer Success, to transform raw data into reliable metrics, reporting, and insights. You will be responsible for ensuring teams have access to accurate, trusted data that scales with the company’s growth.
This role is ideal for someone who combines strong technical analytics fundamentals with deep AI fluency. You should be comfortable using Claude, LLMs, and AI agents to accelerate end-to-end analytics workflows, from requirements gathering and data modeling to analysis, dashboarding, documentation, QA, and automation. At the same time, you should have the technical judgment to read, write, debug, and validate code yourself, knowing where AI can move faster and where human review is essential.
We are looking for someone who knows what best-in-class data and analytics infrastructure looks like, ideally from experience in a scaled, high-performing company, but who is also excited to build in a fast-moving startup environment. You should be nimble, hands-on, and opinionated about when to build versus buy, with the ability to build lightweight internal tools, workflows, and analytics products yourself when that is the fastest or highest-leverage path.
Understand stakeholder data needs across Ops, Finance, and the GTM org, including Sales, Marketing, BDR, and Customer Success, and translate those needs into clear technical requirements.
Define, build, and manage key data pipelines in dbt that transform raw data into canonical datasets.
Use AI tools, LLMs, and agents to accelerate analytics workflows, including data exploration, pipeline development, QA, documentation, dashboard creation, and stakeholder enablement.
Read, write, debug, and validate SQL, Python, dbt models, and AI-generated code to ensure outputs are accurate, reliable, and production-ready.
Establish high data integrity standards, SLAs, and QA processes to ensure timely and accurate data delivery.
Develop reliable dashboards to track core business, GTM, and operational metrics.
Build foundational data products, dashboards, automations, and internal tools that enable self-serve analytics across the company.
Bring a strong point of view on build vs. buy decisions across the GTM data stack, identifying where Oscilar should use off-the-shelf tools versus where we should build internally for speed, leverage, or differentiation.
Partner with GTM and Finance leaders to influence roadmap decisions from a data systems and analytics perspective.
Become an expert in Oscilar’s data models, business metrics, GTM systems, and broader data architecture.
Help shape a modern, AI-native analytics engineering function as Oscilar scales.
5+ years of experience as an Analytics Engineer, Data Engineer, or in a similar Data Science & Analytics role.
Experience partnering with GTM, Finance, and cross-functional leaders to build and report on company-wide metrics.
Strong SQL and Python skills, with the ability to transform raw data into clean, accurate, and scalable data models.
Experience building multi-step ETL workflows and robust data models using tools like dbt.
Strong AI fluency, with hands-on experience using tools like Claude, LLMs, and/or AI agents to accelerate technical analytics, data engineering, automation, or reporting workflows.
Ability to use AI-generated code and analysis effectively while independently reviewing, debugging, and validating the underlying logic.
Familiarity with workflow orchestration tools like Airflow and version control tools like GitHub.
Experience building reporting and dashboards in visualization tools like Hex, Claude-powered workflows, or similar platforms.
Strong data integrity mindset, with experience building reliable data pipelines, metric definitions, QA processes, and reporting standards.
Strong judgment on build vs. buy decisions, with the technical ability and willingness to build lightweight tools, workflows, and automations yourself when needed.
Experience in a scaled, high-performing analytics or data environment, with a clear understanding of what best-in-class looks like.
Full-stack mindset, with a willingness to solve problems end-to-end even when they fall outside a narrow job description.
Compensation: Competitive salary and equity packages, including a 401k plan.
Flexibility: Remote-first culture.
Health: 100% employer-covered comprehensive health, dental, and vision insurance with a top-tier plan for you and your dependents. (US)
Balance: Unlimited PTO policy.
Culture: Family-friendly environment; regular team events and offsites.
Development: Unparalleled learning and professional development opportunities.
Impact: Making the internet safer by protecting online transactions.
