Senior Data Engineer

About the role

Relay is a digital banking platform designed to provide business owners with financial clarity and control. The Senior Data Engineer will own the architecture and scalability of our data ecosystem, ensuring that data pipelines are robust, efficient, and aligned with business needs. This role focuses on building privacy-focused analytical foundations that empower teams to make confident, data-driven decisions. You will be responsible for maintaining high standards of data quality, reliability, and accessibility across the organization. The position requires close collaboration with engineering, product, and governance stakeholders to translate complex requirements into scalable data solutions. You will play a key role in enabling transparency and performance tracking by developing systems that surface meaningful insights. Ultimately, your work will directly support the growth and operational resilience of the Relayfi platform.

Key facts

What you'll do

• Architect and deploy dbt models within Snowflake to enable privacy-conscious analytics and reporting.
• Design and maintain the core data infrastructure that supports Relayfi's digital banking operations and strategic initiatives.
• Optimize complex SQL queries and schemas to ensure high performance, scalability, and clarity across data assets.
• Implement and monitor key performance indicators that provide visibility into product usage, financial health, and operational efficiency.
• Partner with product managers and analysts to translate business questions into structured data workflows and measurable metrics.
• Collaborate with Trust, Security, and Governance teams to embed data safety, compliance, and best practices into every pipeline.
• Lead the improvement of data platform processes, identifying bottlenecks and introducing automation where appropriate.
• Support the integration of modern data tools such as Looker, Metabase, and Mode to enhance self-service analytics for stakeholders.
• Mentor junior engineers and data professionals by providing guidance on coding standards, debugging techniques, and architectural patterns.
• Own end-to-end data workflows from ingestion logic to modeling, ensuring that data remains reliable, documented, and traceable.
• Evaluate and adopt new data technologies and frameworks that align with Relayfi's long-term vision and scalability goals.
• Facilitate cross-functional workshops to align data strategy with business priorities and ensure stakeholder buy-in.
• Monitor data pipelines for anomalies, performance issues, and potential risks, coordinating rapid responses when necessary.
• Contribute to the documentation and knowledge-sharing practices that sustain a high-performing data culture.

Requirements

• Bring a minimum of 3 years of professional experience in Data Engineering within production environments.
• Demonstrate advanced SQL proficiency, including complex window functions, thoughtful schema design, and rigorous query optimization.
• Show fluency in Python with an emphasis on writing clean, maintainable, and production-ready code.
• Provide hands-on experience with dbt, Dagster, Snowflake, and major cloud infrastructure platforms such as AWS, GCP, or Azure.
• Exhibit familiarity with BI and data visualization tools like Looker, Metabase, or Mode to enable effective stakeholder communication.
• Prove the ability to work independently while also collaborating effectively with cross-functional teams in a fast-paced setting.
• Display strong problem-solving skills and a methodical approach to debugging complex data issues under tight deadlines.
• Communicate clearly and professionally, both in writing and during technical discussions with diverse audiences.

Nice to have

• Demonstrate applied statistics knowledge and the ability to create quantitative models that support business decisions.
• Bring domain expertise in SaaS or banking analytics, specifically related to metrics such as LTV, churn, and retention analysis.
• Show experience with microbatch or streaming analytics using frameworks like Spark Streaming, Flink, or Beam, as well as event stores such as Kafka, Kinesis, PubSub, or Pulsar.
• Highlight a background in scaling data systems within early-stage companies where agility and ownership are essential.

Practical notes

• The interview process consists of a 30-minute talent screen, a 45-minute technical deep dive, a 60-minute case study presentation, and a 45-minute in-person leadership interview.
• Compensation offers are based on demonstrated impact and readiness, with no fixed annual review cycle for future compensation adjustments.
• Employment is conditional upon successful completion of a background check and verification through Certn.
• Reasonable accommodations are available upon request during any stage of the hiring process to support accessibility and fairness.