Staff Data Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Design the platform architecture. Establish our warehouse/lakehouse strategy and implement the data lake and layered framework that transforms our raw system of record into reliable, queryable, and intelligence-ready data.

• Develop the data pipelines. Create and manage batch and streaming pipelines that efficiently transfer data from our production systems - utilizing CDC, ELT, and real-time methods where necessary.

• Structure the data. Define the canonical datasets and models relied upon across the organization, ensuring the accuracy, semantics, and contracts are properly established.

• Ensure reliability and precision. As this involves financial data, accuracy is paramount. You will be responsible for data quality, observability, integrity checks, and the testing and monitoring processes that foster trust in the data.

• Construct for a regulated environment. Integrate role-based access, data masking, lineage tracking, and audit capabilities from the outset to safeguard sensitive financial information.

• Facilitate AI/ML and analytics initiatives. Develop the feature pipelines and trustworthy data foundation that support our intelligence efforts, transitioning us from systems of record to systems of intelligence and action.

• Establish standards. Define the practices, tools, and CI/CD processes for data that will be inherited by future teams. You're not just meeting the standard; you're setting it.

⛳️ Requirements

• Over 8 years of experience in developing production data systems, with a proven history of owning architecture and guiding significant decisions through to implementation.

• Proficient in SQL and skilled in Python.

• Extensive experience within at least one modern lakehouse/warehouse ecosystem - for instance, Snowflake with dbt and Fivetran, or Databricks with Spark, Delta Lake, and Unity Catalog. We value depth of knowledge in a specific area and the ability to think critically across technology stacks.

• Strong data modeling expertise - whether dimensional, normalized, or Data Vault - and an understanding of how to design enduring models.

• Familiarity with pipeline orchestration tools (like Airflow, Dagster, Prefect, or similar) and large-scale data processing (such as Spark).

• Hands-on experience in a primary cloud environment (AWS, GCP, or Azure), encompassing security and cost management practices.

• Experience handling sensitive or regulated data, including access controls, encryption, governance, and a keen sense for minimizing the impact of errors.

• A high technical standard upheld through influence and example. You enhance both the work and the performance of those around you, being equally comfortable in the codebase and during design reviews.

🏝️ Benefits

• Competitive salary

• Stock options with the potential to gain additional equity as we expand

• Flexible PTO and paid parental leave

• Medical, dental, & vision insurance

• 401K, HSA, pre-tax savings programs