Employee Applicant Privacy Notice 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. The role: We are seeking a Senior Data Engineer to join our Risk Data Team as a hands-on technical lead supporting Credit, Collections, and Fraud. This role blends deep production data engineering with formal technical and people leadership. You will own architectural decisions for the Risk data platform, define modeling standards, elevate engineering rigor, and build scalable data systems that power risk decisioning across the organization. This role exists to ensure that Risk data pipelines are reliable, well-modeled, observable, and built with long-term maintainability in mind. You will contribute directly to production data pipelines while setting standards for data modeling, dbt architecture, code quality, and observability. This is not an architect-only or strategy-only role — it requires hands-on execution and demonstrated team leadership ownership. What you’ll do: Technical Leadership - Serve as technical lead for the Risk Data Engineering team. - Own architectural decisions and data modeling strategy across the Risk domain. - Define naming conventions, modeling standards, and layered dbt architecture (staging → intermediate → marts). - Lead architecture discussions and technical planning sessions. - Conduct code reviews focused on maintainability, readability, and long-term scalability. - Translate business priorities into well-scoped, production-ready technical deliverables. Production Data Engineering - Design and build production-grade Snowflake data models. - Develop scalable dbt projects, including reusable macros and testing frameworks. - Manage Apache Airflow DAGs, including idempotency, retry logic, and failure handling. - Implement CI/CD best practices for dbt and data pipelines. - Drive automation initiatives to reduce manual operational overhead. Data Modeling - Design dimensional and relational models aligned to business definitions. - Apply modeling best practices including grain declaration, SCD strategies, and surrogate key management. - Balance normalization and performance trade-offs. - Evolve models safely as business requirements change. - Ensure all models are clearly documented with lineage and business logic. Data Quality & Observability - Own the dbt testing framework (schema tests, custom tests, generic tests). - Define and enforce freshness checks, SLA standards, and row-count validations. - Implement monitoring and observability using DataDog. - Proactively identify and reduce reliability incidents. - Establish measurable data quality SLAs in partnership with stakeholders. People Leadership - Participate in hiring, onboarding, and team building. - Run regular 1:1s and provide structured performance feedback. - Develop engineers toward ownership and technical growth. - Address underperformance early and constructively. - Foster a culture of accountability, documentation, and engineering excellence. Collaboration & Stakeholder Engagement - Partner with Risk Data Product Managers, Data Science, ML, and business stakeholders. - Communicate modeling decisions, trade-offs, and pipeline health clearly. - Influence cross-functional technical direction across Risk and platform teams. Operational Excellence - Maintain scalable, secure data systems aligned with enterprise governance standards. - Improve documentation practices including runbooks and architecture decision records. - Contribute to workforce planning and technical roadmap discussions. This role requires collaboration during core business hours. Remote candidates must be able to work cross-functionally with distributed teams. What you’ll need: - Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent work experience). - 8+ years of hands-on data engineering experience. - 2+ years of experience serving as a tech lead or leading engineers formally. - Deep expertise in dimensional and relational data modeling, including SCD strategies and grain design. - Advanced dbt experience, including layered architecture, macros, advanced testing, and semantic layer concepts. - Strong hands-on Snowflake experience, including modeling and performance optimization. - Production-level experience managing Apache Airflow DAGs. - Advanced SQL skills, including query optimization and performance tuning. - Strong Python skills for data pipeline development and automation. - Demonstrated ownership of a data quality and monitoring framework. - Experience working in regulated or high-accuracy environments. - Experience participating in hiring, onboarding, and performance management. - Strong communication skills and ability to influence cross-functional stakeholders. Nice to have: - Experience with Snowflake advanced capabilities (Snowpark, Cortex AI, ML functions). - Familiarity with LLM tooling, RAG systems, or AI-assisted data workflows. - Financial services experience (Credit, Fraud, Collections). - AWS experience (S3, Glue, Lambda) and infrastructure-as-code familiarity. - Experience implementing data governance frameworks at scale. 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. Due to insurance coverage issues, we are unable to accommodate remote work from Hawaii or Alaska 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.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description The Data Engineer role focuses on using data to understand the optimal layout, equipment, processes and software for each unique operation. The Analyst role uses the client’s data set to create actionable information to be used in the design process by design engineers, project managers, and other technical leads. Design analysts create the foundation for projects and sales opportunities by ensuring we are providing data driven results. - Engage directly with clients to learn their business processes and challenges, including on-the-floor observation and employee interviews - Communicate with clients on data needs - Gather and perform analytics on client’s data sets, understanding bottlenecks in operations, inefficiencies, and trends - Manage multiple projects simultaneously at varying phases of completion - Assist the Project Manager/Project lead in creation of client presentations Qualifications - Bachelor’s degree required, with a preference toward Engineering, Supply Chain, Computer Science, or Mathematics - 1-5 years of experience with data analytics - Experience in Microsoft Excel, relational databases (e.g., SQL Server), and other analytical tools - Experience in creating impactful visualizations is a plus - Experience in distribution is a plus Benefits - Health Care Plan (Medical, Dental & Vision) - Retirement Plan (401k, IRA) - Life Insurance (Basic, Voluntary & AD&D) - Paid Time Off (Vacation, Sick & Public Holidays) - Family Leave (Maternity, Paternity) - Short Term & Long-Term Disability - Training & Development - Work From Home Company Description

Data Integration Engineer *** As required by our governmental client, this position requires being a US Citizen. *** ATA LLC is seeking a Data Integration Engineer to support healthcare data integration efforts within an Azure‑based data platform. This role is hands‑on and delivery‑focused, with a strong emphasis on Python‑driven data pipelines, Azure Synapse, and healthcare interoperability (FHIR/HL7). A core expectation of this role is the ability to design, test, and validate data pipelines in environments where upstream specifications may be incomplete or inconsistent. The ideal candidate brings strong technical judgment, repeatable testing strategies, and the ability to raise data quality standards across the team. Location: “Work from Anywhere” in the Continental United States with the ability to travel to the Greater Washington D.C. Metropolitan Area or to a client location from time to time. Our preference is for remote personnel in one of the following locations: Greater Metro Washington DC area and Huntsville, AL. Key Responsibilities - Design, build, and maintain data pipelines in Azure Synapse using Python. - Implement and operate a medallion data architecture (Bronze, Silver, Gold layers). - Ingest, transform, and publish data in CSV, Parquet, and XML formats. - Perform complex data mapping and transformation across healthcare data sources. - Work directly with HL7 and FHIR healthcare data standards. - Define and execute data pipeline testing strategies, including: - Validation of transformations and mappings - Data completeness, accuracy, and consistency checks - Repeatable, team‑adoptable testing approaches - Operate effectively in situations where test cases or specs are not clearly provided, helping establish defensible validation criteria. - Serve as a technical lead, setting patterns and best practices the broader team can follow. - Collaborate closely with engineering, QA, and stakeholders to improve data quality and delivery outcomes. Minimum Requirements: This is a Health IT opportunity and previous experience working in Health IT and with healthcare data and data standards is required. - Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent experience. - 2–5 years of experience in data integration or data engineering roles. - Hands-on experience with HL7, FHIR, X12, or similar healthcare data formats. - Proficiency with Git for version control and collaborative development. - Experience using Terraform to deploy or manage cloud infrastructure. - General knowledge of cloud environments (Azure, AWS, or GCP). - Working knowledge of Azure Synapse or similar cloud data platforms. - Experience working with Parquet file formats in data engineering workflows. - Strong SQL and/or Python skills for data manipulation and validation. General personal traits we know will connect well with the team: - A positive, willing attitude - Self-motivated ability to make and meet commitments. - An ability to think on your feet and solve problems quickly. - Can learn new subject areas on the fly. - Enjoys working in a cross-disciplinary team environment. - Technology agnostic with the ability to apply the right tool to the requirement. About ATA: A leading provider of full-stack data and AI solutions with deep mission experience supporting the Department of Defense, Department of Homeland Security, IC, and federal agencies. Founded in 2008 and headquartered in Virginia, ATA specializes in secure, scalable, and operationally ready technologies that transform data into actionable insight. With a proven track record in advanced analytics, software development, and technology integration, ATA is uniquely positioned to accelerate federal organizations in a number of ways by leveraging powerful contracting options and delivering cutting-edge, automation-enhanced, AI-assisted capabilities—tailored to need and mission assurance imperatives. We believe our diversity of infrastructure, data, and application experience is valuable and is one of the attributes that sets ATA apart. Summary of Benefits: We expect each member of our team to fully engage creatively and work collaboratively and perform each day to the best of their ability. To support this, we have created a benefit package focused on professional growth, achieving a healthy work-life balance, and participation in the long-term success of the company. Our benefits include: generous paid time-off; an employee incentive program; continuous learning culture, Internal Investment Projects (IIP), virtual brown-bags/level-ups, and other professional development activities; recruiting bonuses; 3% 401k Safe Harbor contributions; Medical/Dental/Vision, Long & Short-term Disability, AD&D insurance, and Life Insurance. #CherokeeFederal #LI-RG1 #LI-Remote

• Identifying, creating, preparing data required for modern Data solutions and Data for AI. • Designing ETLs and ELTs for data transformations. • Designing, building and management of Datalakes architectures. • Working with Apache Hadoop projects (Spark, Hive, Pig, Oozie, Airflow, etc). • Integrating and testing Data solutions. • Creating and documenting the tests to meet requirements. • Working with Bigdata Environments. • Managing Data Cloud services, data access, security and data governance. • Analyzing and prepare data for Machine Learning workloads. • Managing monitoring and logs of applications and services.