Senior Data Engineer- People Analytics

SoFi is hiring a Senior Data Engineer- People Analytics . Level rates it ; you can apply on SoFi.

Level's read on this job

On Level's scale, this data engineer role is Little AI ●○○○: aI is not part of the work.
Tools and skills the posting cites include AI agents.
Level classifies it as a senior role.
Level lists 46 other open roles at SoFi.

AI in this role

ai-agents

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Who we are:

Shape a brighter financial future with us.

Together with our members, we’re changing the way people think about and interact with personal finance.

We’re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world.

Role Summary:

We are looking for a talented and detail-oriented Sr Data Engineer to tackle data challenges. You will design, build, and maintain critical data pipelines and datasets, supporting areas like recruiting, compensation, talent management, and learning and development. Your work will enhance data accessibility and empower the People Team and business leaders to make informed decisions with high-quality, reliable data.

Key Responsibilities:

Develop and maintain robust data pipelines and datasets.
Build foundational data products for key business areas.
Enhance self-service data capabilities for the People Team.
Ensure high standards in ETL/ELT operations, data quality, and pipeline reliability.

Join us to drive impactful change and support SoFi's mission of fostering a thriving workplace through data excellence.

What you’ll do:

Design and build production dbt models in Snowflake that integrate Workday and other People systems into well-modeled, documented datasets, including slowly changing dimensions for People history.
Build and operate Airflow DAGs that ingest People systems data and orchestrate dbt runs, keeping loads reliable and re-runnable.
Own data quality and observability: dbt tests, freshness checks, row-count validation, and monitoring so issues are caught before stakeholders see them.
Implement and maintain row-level security and access controls for People data, partnering with governance and security teams.
Build and maintain semantic models and AI agents, held to the same modeling, access, and quality standards as our pipelines.
Collaborate with cross-functional teams, such as other data engineers, people analysts, data scientists, and business stakeholders, to translate requirements into production-ready deliverables, and communicate technical trade-offs to non-technical partners.
Enforce data governance policies and practices to maintain data integrity, security, and compliance with relevant regulations.
Mentor other engineers through code review, modeling standards, and technical guidance.

What you’ll need:

A bachelor's degree in Computer Science, Data Science, Engineering, or a related field.
5+ years of experience in data engineering and analytics technical strategy.
Proficiency in data engineering tech stack: Python / SQL / dbt / Airflow / Gitlab .
Proficiency in relational and cloud database platforms such as Snowflake, Redshift, or GCP
Thorough knowledge of data modeling, database design, data architecture principles, data operations, and CI/CD.
Strong analytical and problem-solving abilities, with the capability to simplify complex issues into actionable plans.
Experience with sensitive or regulated data and access controls.
Experience with AI agents or LLM-assisted analytics, and with the HR / People function, is advantageous.

Compensation and 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. To view all of our comprehensive and competitive benefits, visit our Benefits at SoFi page! SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law. The Company hires the best qualified candidate for the job, without regard to protected characteristics. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. New York applicants: Notice of Employee Rights SoFi is committed to an inclusive culture. As part of this commitment, SoFi offers reasonable accommodations to candidates with physical or mental disabilities. If you need accommodations to participate in the job application or interview process, please let your recruiter know or email accommodations@sofi.com. We are unable to accommodate remote work from Hawaii, Alaska or Puerto Rico at this time. Internal Employees If you are a current employee, do not apply here - please navigate to our Internal Job Board in Greenhouse to apply to our open roles.

How we rate this

Senior Data Engineer- People Analytics at SoFi rates 19 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.