Senior Analytics Engineer

Accountabilities: You will take ownership of the data architecture and engineering practices that ensure high-quality, dependable information across the organization. Your main responsibilities will include: Design, build, and maintain scalable data pipelines and architecture across Snowflake, dbt, and AWS. Lead technical decisions related to data ingestion, transformation, orchestration, and modeling, ensuring infrastructure remains reliable and performant. Develop and improve integrations with internal systems, merchants, payment platforms, and external partners. Build reusable dbt models that transform raw source data into trusted datasets for analytics, financial systems, and reporting. Establish data quality and governance standards, including automated testing, monitoring, validation, documentation, and traceability. Collaborate with Engineering, Analytics, Finance, Operations, and Financial Systems teams to define data requirements and deliver reliable datasets. Communicate technical concepts, architectural decisions, and trade-offs clearly to technical and non-technical stakeholders. Promote engineering best practices across SQL, dbt, code reviews, CI/CD, and collaborative development workflows. Identify opportunities to optimize performance, simplify architecture, and reduce manual processes. Mentor teammates, provide technical guidance, and contribute to the long-term evolution of the analytics engineering platform. Integrate AI coding agents and tools into development workflows, identifying opportunities for reusable skills, automated checks, and agent-driven processes while carefully reviewing AI-generated code. Requirements : The ideal candidate combines extensive SQL and data engineering expertise with strong architectural judgment, independent problem-solving skills, and the ability to lead technical initiatives. The following qualifications are expected: 5+ years of experience as an Analytics Engineer, Data Engineer, or in a similar role focused on data infrastructure, analytics, or operational data. Expert-level SQL skills, including complex joins, window functions, advanced transformations, and query optimization across large datasets. Strong production experience with dbt and cloud data warehouses, preferably Snowflake, including data modeling, testing, documentation, and deployment practices. Deep understanding of data modeling principles, including dimensional modeling, facts and dimensions, slowly changing dimensions, and incremental models. Experience designing and maintaining end-to-end data pipelines, from source ingestion through transformation and downstream reporting. Proficiency with Git-based development workflows, including pull requests, code reviews, CI/CD, and collaborative development practices. Hands-on experience building with AI coding agents, such as Claude Code. Ability to independently lead technical projects, make sound architectural decisions, and balance immediate business priorities with long-term platform improvements. Strong communication skills and the ability to collaborate effectively with both technical and non-technical teams. Nice-to-have qualifications: Experience with AWS data services such as S3, Glue, and Lambda. Familiarity with infrastructure as code and cloud-based data architecture. Working knowledge of Python for automation and validation, although the role is primarily focused on SQL and dbt. Experience working with APIs and external data integrations. Benefits : Annual base salary range of $155,000–$170,000, depending on job-related knowledge, skills, and experience. Full medical, dental, and vision coverage. Stock opportunities in a growing early-stage startup. Generous and flexible paid time off policy. 401(k) plan with financial guidance from Morgan Stanley. Collaborative and supportive work environment with colleagues from diverse backgrounds. Remote work arrangement.