Analytics Engineer

Key Responsibilities

• 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.
• Help shape a modern, AI-native analytics engineering function as Oscilar scales.

Required Qualifications

• 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.