Build and maintain data platforms to enable data reliability, data integrity, and data governance, enabling accurate, consistent, and trustworthy data sets.
Conduct requirements gathering and project scoping sessions with subject matter experts, business users, and executive stakeholders to discover and define business data needs.
Design, build, and optimize the data architecture and Extract, Transform, and Load (ETL) pipelines.
Work closely with analysts to productionize and scale value-creating capabilities, including data integrations and transformations, model features, and statistical and machine learning models. Engage with the analyst community, understand critical user journeys and data sourcing inefficiencies, advocate best practices and lead analyst trainings.
Write and review end-user and technical documents, including requirements and design documents for existing and future data systems, as well as data standards and policies.
Minimum qualifications:
Bachelor's degree or equivalent practical experience.
5 years of experience designing data pipelines, and dimensional data modeling for synch and asynch system integration and implementation using internal (e.g., Flume, etc.) and external stacks (DataFlow, Spark, etc.).
5 years of experience coding in one or more programming languages.
5 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.
Preferred qualifications:
Master’s degree in a quantitative discipline (e.g., Computer Science, Engineering, Statistics, Math).
Experience with data warehouses, large-scale distributed data platforms, and data lakes.
Ability to navigate ambiguity in a fast-paced environment with multiple stakeholders.
Excellent structured thinking skills, with the ability to break down complex, multi-dimensional problems.
Excellent business and technical communication, organizational, and problem-solving skills.
