Staff Data Engineer

July 10

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SoFi

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SoFi helps you save, spend, earn, borrow, invest, and protect your money–all in one app. NMLS 1121636

Refinancing Student Loans • Student Loan Consolidation • Mortgages • Mortgage Refinancing • Personal Loans

1001 - 5000

Description

• SoFi is seeking an experienced and motivated Staff Data Engineer to drive high-standard technical solutions for the Data Products team within the SIPS (Spend, Invest, Protect, Save) division - supporting all SoFi Financial Services. • A talented, enthusiastic, detail-oriented, and experienced Data Engineer who knows how to take on big data challenges in an agile way. • Design and develop robust data architectures and data pipelines to support data ingestion, processing, storage, and retrieval. • Optimize data engineering systems and processes to handle large-scale data sets efficiently. • Collaborate with cross-functional teams, such as data scientists, software engineers, and business stakeholders, to understand data requirements and deliver solutions that meet business needs. • Enforce data governance policies and practices to maintain data integrity, security, and compliance with relevant regulations.

Requirements

• A bachelor's degree in Computer Science, Data Science, Engineering, or a related field • 8+ years of experience in data engineering and analytics technical strategy • Proficiency in data engineering tech stack; Snowflake / PostgreSQL / Python / SQL / GitLab / AWS / Airflow/ DBT and others.. • Proficiency in relational database platforms and cloud database platforms such as Snowflake, Redshift, or GCP • Strong in Python and/or another data-centric language. • Thorough knowledge of data modeling, database design, data architecture principles, and data operations. • Strong analytical and problem-solving abilities, with the capability to simplify complex issues into actionable plans. • Experience in the Fintech industry is advantageous.

Benefits

• The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate’s experience, skills, and location.