Your Role:
We are looking for a passionate Data Engineer to help build an operating system for wealth management. In this role, you would be responsible for building robust data systems by proposing new solutions, evaluating design approaches, and prototyping technologies. You will be heavily involved in architecture optimization, building data warehouse and data pipelines as well as expanding analytics capabilities.
You would work alongside other engineers, product managers, designers, and key stakeholders to build and execute product and technology roadmaps. We would rely on you to build a strong culture, define best practices, help the other engineers in your team grow, and facilitate the work of your team.
If you enjoy collaborating with a team, being innovative, and are excited about an opportunity to work within a fast-growing company, we would like to speak to you!
What you'll do:
Be a technical evangelist within the engineering teams and at Purpose in general.
Architect and build a data platform including pipelines, reconciliation engines, and internal data tools.
Influence the working culture of the team and champion innovation.
Build high-quality and scalable data warehouses.
Build analytics abilities to facilitate decision-making.
Promote an environment of collaboration and innovation.
What you'll bring:
3+ years' experience in building data systems.
Solid understanding of relational databases and data warehouses and best practices around data manipulation, data storage, and data visualization.
Solid understanding of Data management including ELT and ETL.
Strong analytical and data-wrangling skills.
Experience with Snowflake, Big Query, Redshift, or similar cloud data warehouse technologies.
Experience with dbt or SQLMesh
Strong programming skills in SQL, Python, or other modern languages.
Experience with cloud native infrastructure (AWS, Docker, Kubernetes, etc.)
Bonus Points:
Prior experience in financial services/ wealth management.
Understanding of regression modeling.
Building and scaling ML solutions.
Familiarity with data governance and PII management.
Experience with dimensional data modelling
