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 We’re looking for a Senior Data Engineer to design, build, and optimize our data platforms so teams across the company can make fast, reliable, data-driven decisions. You’ll be a key technical leader, owning end-to-end data pipelines and modeling, and setting best practices around how we work with data. You’ll work heavily with Databricks, Spark, SQL, and Python, building scalable data solutions that power analytics, reporting, and data products. Experience in payments or financial services is a strong plus. What You’ll Do - Design and build data pipelines - Develop, maintain, and optimize ETL/ELT pipelines on Databricks and Spark - Integrate data from multiple internal and external sources into a centralized data platform - Own data modeling & architecture - Design and maintain robust data models (e.g., star/snowflake schemas, data vault, dimensional models) to support analytics and self-service BI - Establish and enforce data modeling standards and documentation - Ensure data quality, reliability, and performance - Implement data quality checks, validation frameworks, and monitoring - Tune queries and jobs for performance and cost efficiency in Databricks and downstream systems - Collaborate and lead - Partner with data analysts, data scientists, and product/engineering teams to understand data needs and translate them into technical solutions - Provide technical leadership and mentorship to other data engineers; help review designs and code - Governance & best practices - Contribute to and refine our data governance, security, and access control practices - Drive best practices around version control, CI/CD for data, and code standards Qualifications - 7+ years of professional experience as a Data Engineer, Software Engineer, or similar role - Strong hands-on experience with Databricks (or a very similar cloud data platform) including cluster management, jobs, and notebooks - Advanced experience with Apache Spark for batch and/or streaming data processing - Expert-level SQL skills (complex joins, window functions, query optimization) - Strong Python skills for data engineering (e.g., PySpark, data processing libraries, scripting) - Proven experience in data modeling and designing schemas for analytics and reporting - Experience building and maintaining data pipelines in a cloud environment (AWS, Azure, or GCP) - Strong understanding of data warehousing concepts, ETL/ELT best practices, and data lifecycle - Solid software engineering fundamentals: version control (git), testing, code reviews, and CI/CD - Excellent communication skills and the ability to collaborate with technical and non-technical stakeholders Requirements - Experience in payments, fintech, banking, or broader financial services (e.g., transaction data, ledgers, risk, fraud, reconciliation) - Experience with streaming technologies (e.g., Spark Structured Streaming, Kafka, Kinesis, or similar) - Familiarity with dbt or similar transformations-as-code frameworks - Experience with orchestration tools (e.g., Airflow, Databricks Workflows) - Knowledge of BI tools (e.g., Power BI, Tableau, Looker) and how data models power them - Exposure to machine learning workflows and supporting data science teams - Experience implementing data governance, lineage, and catalog tools What You’ll Bring - A product mindset: you think about the end users of data and build with usability in mind - A bias for automation, reliability, and scalability over one-off solutions - Comfort with ambiguity, ownership of complex problems, and a desire to continuously improve the data ecosystem Benefits - 401k investment opportunity, with company match - Medical, Dental, and Vision Plans - Paid Time Off - Paid Parental Leave - Paid Volunteer Leave - Fully Remote Work environment Role Information Full-Time, Remote in the United States. Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time. Company Description Ever wondered how your favorite local shops compete with the big guys? That’s where we come in. We’re Quilt Software, providing Main Street's unsung heroes – from quirky cheese shops to family-run jewelry stores – with the tools they need to compete. Last year, we helped 14,000+ shops make over $2 billion in sales with our family of industry-specific software solutions. If you get a kick out of supporting local businesses, love great software, and want to be part of a company that’s powering Main Street, we’d love to chat. Come join us in our quest to keep local retail not just alive, but thriving!

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Ergotron, Inc. has an opening for a Sr. Data Integration Engineer in Eagan, MN. 40 hrs/week. Job duties include: - Create, maintain, monitor and support optimal application integration solutions leveraging API’s, web services and system-specific functionality to execute business processes - Design, develop and deploy solutions for batch and continuous (near-real time) data integration solutions - Design and implement solutions using API-led Integration and Agile Development processes - Administer, monitor and support data movement infrastructure (Informatica environments, ETL and IOT processes, scripts and utilities) - Work closely with Oracle ERP, Salesforce and Power BI Developers, Architects and Analysts to implement appropriate data integration and ETL processing solutions - Manage ETL defects, enhancements and new functionality requests to meet changes in systems and business needs - Identify and resolve problems with data processing infrastructure, processes, systems and tools - Recommend solutions for improvement and standardization - Assist with leveraging Informatica Intelligent Cloud Services new features, software installation, upgrade and patching - Lead DevOps process maturity supporting a Continuous Integration strategy across key systems in the IT portfolio - Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs - Engage with colleagues to implement End to End digital solutions that utilize cloud, AI, automation, IoT, API and other multi-vendor capabilities - Support “Lean Business Enterprise” initiatives for continuous process improvement and waste elimination Qualifications - Bachelor’s degree, or foreign equivalent, in Engineering, Computer Science, Information Systems, or related IT field - 5 years of experience as a Data Integration Developer/Engineer or in a related role/capacity engaged in the design, analysis, and development of Databases, Reporting, Data Warehouse, ETL Design, and BI applications - 5 years of experience developing application integration solutions leveraging SaaS applications and their API’s or requisite methods to enact enterprise integration patterns - 3 years of experience developing Informatica Intelligent Cloud Services integration solutions, including those involving Salesforce and Oracle ERP - 5 years of experience utilizing Informatica Intelligent Cloud Services (IICS) - 5 years of experience utilizing relational SQL databases, including Oracle and SQL Server - 3 years of experience utilizing Salesforce - 3 years of experience utilizing Change Data Capture methods for data integrations - 3 years of experience utilizing object-oriented/object function scripting languages: Python, Java, or Java Script - 5 years of experience developing application message solutions leveraging REST services, JSON, and XML Benefits - Your comprehensive health plan begins your first day of work - A choice of three medical plans to meet your unique needs - Flexible spending accounts - Prescription drug benefits - Vision and dental coverage - Wellness benefits - 401(k) savings plan - Progressive parental and grandparent leave - Basic Term Life and Accidental Death & Dismemberment Insurance - Critical Illness, Hospital Indemnity, and Accident Insurance - Optional Term Life Insurance - Short Term and Long Term Disability - Generous paid time off and holidays - Educational assistance - Employee assistance program - Group Legal Insurance - Pet Insurance - 401K with match - 5 weeks of Paid time off

• Design, own, and maintain core enterprise data models. • Provide architectural leadership by defining and enforcing data engineering standards and best practices. • Implement and manage Snowflake security and RBAC, ensuring secure, scalable access across analytics and AI use cases. • Build and maintain reusable dbt macros, seeds, and utility functions. • Leverage advanced dbt features including incremental models, snapshots, and exposures. • Optimize and refactor complex SQL for performance, reliability, and maintainability. • Implement and maintain CI/CD workflows (Git branching, automated testing, code reviews). • Monitor Snowflake warehouse and query performance. • Drive Snowflake cost optimization initiatives (warehouse sizing, caching, workload management). • Mentor and support junior data engineering resources. • Partner with leadership to co-own Snowflake COE and data governance practices.