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Constantinople is building an AI-native banking platform that delivers fully-managed software and operational infrastructure for banks. The platform consolidates customer experience, product, data, operations, compliance, and infrastructure into a single system, replacing manual processes with AI at scale.
As a Senior Analytics Engineer, you will design, build, and maintain the data models and transformation pipelines that power client reporting, regulatory reporting, and analytics across the business. You will work closely with Product, Risk, Finance, and Client Delivery teams to transform raw platform data into clean, well-modeled datasets that the entire company relies on. The analytics stack is built on dbt and Snowflake.
Key responsibilities include:
- Design, build, and maintain dbt models that transform raw data into clean, well-documented, analysis-ready datasets
- Write and optimize SQL in Snowflake, ensuring models remain performant and cost-efficient as data volumes grow
- Own data quality by implementing tests, documentation, and validation checks that catch issues before they reach downstream consumers
- Partner with cross-functional teams to understand reporting needs and translate them into reusable data models
- Support and troubleshoot data issues affecting live customer reporting, including on-call rotation participation
- Support delivery of key metrics used in board, client, and regulatory reporting
- Help maintain and evolve modeling standards, naming conventions, and dbt project structure as the business scales
- Collaborate with data and platform engineers on upstream pipelines, flagging data quality or structural issues
- Participate in code reviews to improve model quality, testing, and maintainability
You will own meaningful pieces of work end-to-end in a small team and help set standards for how the team builds.
REQUIREMENTS:
- Hands-on experience building and maintaining data models in dbt, including tests and documentation
- Strong SQL skills with experience writing and optimizing queries in Snowflake or similar cloud data warehouse
- Solid understanding of data modeling concepts such as dimensional modeling and star schemas, with ability to apply them to real-world messy data
- Experience with orchestration tools such as Airflow to schedule and monitor pipelines
- Strong communication skills and comfort working directly with non-technical stakeholders
- Detail-oriented with a focus on data quality and proactive issue identification
- Comfortable in fast-paced startup environments, pragmatic about scope and sequencing, and careful with decision-critical data
