Role Description Buscamos un(a) Arquitecto de Datos con más de 3-5 años de experiencia, responsable de definir la estrategia de arquitectura de datos de la organización, diseñar arquitecturas empresariales de alto nivel y liderar la implementación de soluciones de datos complejas, seguras, escalables y alineadas con los objetivos estratégicos del negocio. - Definir la estrategia de arquitectura y gobierno de datos de la organización. - Diseñar arquitecturas de datos empresariales y liderar su implementación. - Establecer estándares, lineamientos y mejores prácticas de arquitectura de datos. - Evaluar e incorporar nuevas tecnologías de datos. - Garantizar seguridad, calidad, capacidad, rendimiento y cumplimiento normativo. - Liderar iniciativas de Big Data, Data Warehousing, integración y calidad de datos. - Colaborar con líderes técnicos en la estrategia tecnológica corporativa. - Realizar análisis de impacto y definir evolución de la arquitectura de datos. - Impulsar innovación en datos, analítica, IA/ML y otras capacidades emergentes. - Liderar equipos y participar en reportes e iniciativas estratégicas. Qualifications - Más de 3-5 años de experiencia en arquitectura de datos. - Conocimiento profundo en arquitectura de datos empresariales, Big Data y Data Warehousing. - Experiencia en diseño de soluciones sobre AWS, Azure y GCP. - Conocimiento avanzado en seguridad de datos, cumplimiento normativo, calidad de datos, metadatos y datos maestros. - Experiencia en integración de datos compleja y entornos ágiles. - Conocimiento en arquitecturas de datos para IA/ML, IoT, Blockchain, serverless, edge computing y deseable computación cuántica. - Manejo de herramientas como TOGAF, Zachman, Snowflake, Databricks, Informatica PowerCenter, IBM InfoSphere, Collibra, Alation, Tableau Server, Power BI Premium, Imperva, Varonis, Informatica MDM, Profisee, Airflow y Control-M. Requirements - Liderazgo estratégico. - Visión de negocio. - Comunicación y negociación a nivel ejecutivo. - Influencia y capacidad de movilizar equipos. - Pensamiento crítico y resolución de problemas complejos. - Gestión de riesgos. - Innovación y creatividad.

Role Description Data Engineer gains access to data across the organization and provides ongoing analysis of the data by monitoring, profiling and analyzing databases. Requires a mix of functional, data and technical skills. The right candidate must be able to understand business requirements, translate them into information needs and implement those requirements using data available. The hire will be responsible for expanding and optimizing data architecture, as well as optimizing data flow and collection for cross functional teams. The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems. The Data Engineer will support our software developers, database architects, and data analysts and data scientists on data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects. - Assemble large, complex data sets that meet functional / non-functional business requirements. - Strong knowledge of SQL required. Ability to identify sets and subsets of information across multiple joins or unions of tables is preferred in addition to writing and troubleshooting SQL queries for data mining. - Perform complex data analysis and investigation for customer requests to explain results and to make appropriate recommendations. - Strong understanding of data modeling concepts. - Problem solver with the initiative to think critically to identify improvement opportunities (error detection, error correction, root cause analysis). - Understand ETL that will aid in verification and testing of data. - Build processes supporting data transformation, data structures, metadata, dependency and workload management. - A successful history of manipulating, processing and extracting value from large, disconnected datasets. - Analyze business objectives and develop data solutions to meet customer needs. - Demonstrated ability to effectively participate in multiple, concurrent projects. - Improve and customize current data solutions to meet business functional and non-functional requirements. - Research new and existing data sources in order to contribute to new development, improve data management processes, and make recommendations for data quality initiatives. - Perform periodic data quality reviews for internal and external data. - Ensure timely resolution of queries and data issues. - Look for new ways to find and collect data by researching potential new sources of information. - Work with data and analytics experts to strive for greater functionality in our data systems. Benefits - Medical: Multiple plan options. - Dental: Delta Dental or reimbursement account for flexible coverage. - Vision: Affordable plan with national network. - Pre-Tax Savings: HSA and FSAs for eligible expenses. - Retirement: Competitive retirement package to secure your future. Company Description Mayo Clinic is top-ranked in more specialties than any other care provider according to U.S. News & World Report. As we work together to put the needs of the patient first, we are also dedicated to our employees, investing in competitive compensation and comprehensive benefit plans to take care of you and your family, now and in the future. And with continuing education and advancement opportunities at every turn, you can build a long, successful career with Mayo Clinic. - Today, our employees are located at our three major campuses in Phoenix/Scottsdale, Arizona, Jacksonville, Florida, Rochester, Minnesota, and at Mayo Clinic Health System campuses throughout Midwestern communities, and at our international locations. - Each Mayo Clinic location is a special place where our employees thrive in both their work and personal lives.

Role Description The Lead Data Engineer on the Nebula team plays a significant technical leadership role in shaping and scaling the data foundation that powers analytics, reporting, AI development, and operational decision-making across the organization. This role combines hands-on data engineering execution with practical team leadership, helping the organization build reliable, flexible, and production-ready data systems. The Lead Data Engineer heads a lean, high-caliber squad of data engineers, while remaining deeply hands-on in the design, development, and operation of core data systems. The role balances direct technical contribution with mentoring, coaching, coordination, and day-to-day support for the engineers on the squad. Working across ingestion, transformation, storage, modeling, orchestration, and delivery, this role partners closely with Product, Engineering, AI, Analytics, and domain Subject Matter Experts (SMEs) to translate complex business processes into scalable data platforms, pipelines, and trusted datasets. This role owns the technical direction for core data capabilities, including ETL/ELT, batch and real-time processing, OLTP and OLAP systems, BI-ready data models, and cloud-based data infrastructure in a regulated, high-stakes environment. Success requires strong architectural judgment, operational discipline, and the ability to raise the technical bar for both systems and people. This is a fully remote position that offers a competitive salary range of $220,000 to $300,000, plus an annual bonus. You'll also receive our excellent benefits package, which includes medical coverage starting on day one and a company-matched 401(k). Compensation may vary based on experience, location, and other job-related factors. Responsibilities - Strategic Technical Leadership - Own the architecture and evolution of core data systems, including ingestion, transformation, orchestration, storage, modeling, and delivery layers - Set technical direction for ETL/ELT, batch processing, real-time pipelines, OLTP and OLAP systems, and BI-ready data assets - Make pragmatic architecture decisions that balance scalability, reliability, security, performance, cost, and delivery speed - Establish engineering standards, reusable patterns, and design principles that improve quality and leverage across the data platform - Hands-On Data Engineering Delivery - Lead the design, build, rollout, and operations of greenfield data infrastructure - Build and maintain complex data pipelines across diverse source and destination systems, including databases, APIs, files, SaaS platforms, event streams, and internal applications - Design and optimize data models, warehouse schemas, semantic layers, and curated datasets for analytics, reporting, AI, and product use cases - Contribute directly to critical implementation work, including writing code, code and design reviews, migrations, reliability improvements, and production issue resolution - Squad Leadership & Management - Lead a lean, high-caliber squad of data engineers, spending focused time mentoring, coaching, managing, and coordinating the team - Develop engineers through regular feedback, technical guidance, code reviews, career support, and clear expectations around quality and ownership - Help prioritize team work, clarify scope, remove blockers, and ensure the squad delivers reliably against business and technical goals - Contribute to hiring, onboarding, performance development, and team operating rhythms as the data engineering function grows - Cloud Platform & Production Operations - Deploy, operate, and improve data pipelines, data stores, and supporting infrastructure on major cloud platforms such as AWS, GCP, or Azure - Drive strong practices for CI/CD, infrastructure-as-code, automated testing, monitoring, alerting, and incident response - Ensure data systems are observable, fault-tolerant, recoverable, and maintainable in production - Identify opportunities to reduce operational toil, improve platform reliability, and manage cloud infrastructure costs effectively - Data Quality, Governance & Trust - Define and enforce standards for data quality, validation, reconciliation, lineage, schema evolution, metadata, and documentation - Establish patterns for data contracts, ownership, SLAs, and runbooks that help downstream teams trust and use data confidently - Partner with security, compliance, and business stakeholders to support privacy, auditability, access controls, and regulated data handling - Raise the maturity of data governance and reliability practices without slowing down pragmatic delivery - Cross-Functional Partnership - Partner closely with Product, Engineering, AI, Analytics, and business stakeholders to align data architecture with organizational priorities - Translate ambiguous business needs and operational workflows into clear technical plans, milestones, and production-ready solutions - Serve as a senior technical point of contact for data-heavy initiatives, communicating tradeoffs, risks, sequencing, and timelines clearly - Enable downstream consumers, including analysts, product teams, data scientists, and operational users, through reliable and well-modeled data assets - Culture & Craft - Contribute to a culture of ownership, curiosity, operational rigor, pragmatism, and engineering excellence - Raise the bar for the team through thoughtful design, clear abstractions, strong reviews, and sound technical judgment - Balance staff-level technical depth with practical people leadership, helping the team grow while continuing to ship high-quality systems Qualifications - 5-8+ years of experience building and operating production-grade data pipelines, platforms, and distributed data systems - 2+ years of experience leading, mentoring, or managing data engineers in a tech lead, staff-level project lead, engineering manager, or TLM capacity - Strong hands-on experience with industry-standard tools and platforms for ETL/ELT, orchestration, data warehousing, streaming, and BI - Deep understanding of OLTP and OLAP systems, including the ability to design architectures that support transactional, analytical, and operational workloads - Experience building flexible data pipelines across many source and destination types, including databases, APIs, files, queues, event streams, SaaS platforms, and internal systems - Strong experience with both batch and real-time processing patterns, including tradeoffs in latency, reliability, cost, and operational complexity - Experience deploying and operating cloud-based data infrastructure on AWS, GCP, or Azure - Advanced SQL and data modeling expertise, including schema design, warehouse optimization, semantic modeling, and performance tuning - Strong programming ability in languages commonly used in data engineering, such as Python, Java, Scala, Go, or similar - Comfort with CI/CD, infrastructure-as-code, automated testing, observability, incident response, and production operations for data systems - Strong architectural judgment in ambiguous environments where systems must balance speed, reliability, compliance, maintainability, and long-term leverage - Clear communication skills with both technical and non-technical teammates, including the ability to explain tradeoffs and influence direction Preferred Experience - Experience operating as a Technical Lead or Tech Lead Manager responsible for both technical implementation, technical direction, and people development - Experience with modern orchestration and transformation tools such as Airflow, Dagster, dbt, or similar platforms - Experience with cloud-native warehouses or lakehouse platforms such as Snowflake, BigQuery, Redshift, Databricks, or equivalent technologies - Experience with streaming systems such as Kafka, Kinesis, Pub/Sub, Flink, Spark Streaming, or similar technologies - Experience enabling BI and self-service analytics through curated datasets, semantic layers, and reporting platforms such as Looker, Tableau, Power BI, or similar tools - Experience building data platforms that support AI, machine learning, decisioning, or LLM-powered workflows - Experience scaling a data engineering function, including technical standards, operating rhythms, hiring, onboarding, and team development - Experience in fintech, mortgage, lending, payments, insurance, or other regulated domains A Note to Candidates You do not need prior fintech or finance experience to succeed in this role. If you are a senior data engineer with strong architectural judgment, a hands-on builder mindset, and the ability to develop other engineers, we would love to hear from you. If your background does not line up perfectly with every bullet, but this role feels like the kind of work you want to do, please apply. Bayview is an Equal Employment Opportunity employer. All aspects of consideration for employment and employment with the Company are governed on the basis of merit, competence and qualifications without regard to race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, or any other category protected by federal, state, or local law. #LI-Remote

Role Description The Data Engineer, Mortgage Servicing on the Nebula team acts as the mortgage servicing data subject matter expert and plays a critical role in building and evolving the data foundation that powers analytics, reporting, AI development, and operational decision-making across the organization. This role is responsible for designing, building, and maintaining reliable, scalable, and flexible data systems that support a wide range of internal and external use cases. - Requires domain awareness in mortgage and servicing-related data environments. - Understanding of the complexities associated with loan-level lifecycle data, transaction processing, cash movement, and reconciliation across systems. - Must be able to translate business workflows and system behavior into accurate, auditable data structures that support downstream reporting, operational processes, and regulatory requirements. - Contributes to the development and evolution of core data capabilities, including batch and real-time pipelines, operational and analytical data stores, semantic models, and BI-ready datasets. - Expected to operate effectively in a modern engineering environment, using automation, observability, and infrastructure-as-code practices. - Help enable downstream analytics, reporting, product capabilities, and AI systems by ensuring that data is trustworthy, accessible, and fit for purpose. Responsibilities - Data Pipeline Development: - Design, build, and maintain robust data pipelines for a wide variety of input and output sources, including internal systems, third-party platforms, files, APIs, event streams, and databases. - Develop scalable ETL and ELT workflows for both batch and real-time processing. - Ensure pipelines are reliable, testable, observable, and easy to extend as business needs evolve. - Build reusable data integration patterns that support growing volumes, new source systems, and downstream consumers across analytics, applications, and AI initiatives. - Data Platform & Storage: - Design and manage data architectures that support OLTP, OLAP, and reporting workloads across operational and analytical environments. - Build and optimize data models, warehouse schemas, and curated datasets for analytics and BI use cases. - Contribute to the design and operation of modern data platforms, including warehouses, lakehouses, streaming systems, and supporting orchestration frameworks. - Help define patterns for data storage, partitioning, performance optimization, retention, and lifecycle management. - Servicing-Oriented Data Modeling & Integrity: - Design and maintain data models that accurately reflect loan-level lifecycle events, including payment activity, balances, adjustments, and status changes. - Ensure consistency and reconciliation across systems where transactional, financial, and reporting data must align. - Identify and resolve discrepancies across source systems, and build data structures that support accurate, auditable outputs for downstream operational processes, reporting, and decisioning. - Cloud Deployment & Operations: - Deploy, operate, and improve data pipelines and data stores on major cloud platforms such as AWS, GCP, or Azure. - Use infrastructure-as-code, CI/CD, and automation practices to improve deployment speed, consistency, and reliability. - Monitor production data systems using logging, alerting, and observability tooling to proactively identify and resolve issues. - Support secure, resilient, and cost-conscious operation of cloud-based data infrastructure. - Data Quality, Reliability & Governance: - Implement data quality checks, validation rules, reconciliation processes, and monitoring to ensure trustworthy data across systems. - Establish and maintain standards for lineage, documentation, metadata, schema evolution, and operational runbooks. - Partner with stakeholders to improve data accessibility, consistency, and usability while maintaining appropriate controls and governance. - Contribute to practices that support security, privacy, auditability, and compliance in a regulated environment. - Cross-Functional Collaboration: - Partner closely with Product, Engineering, and business stakeholders to understand data needs, workflows, and constraints. - Translate business and operational requirements into clean, scalable, and maintainable data solutions. - Support downstream consumers of data, including analysts, researchers, product teams, and operational users. - Communicate clearly with both technical and non-technical stakeholders about data availability, quality, tradeoffs, and delivery timelines. - Iteration & Continuous Improvement: - Continuously improve pipeline performance, reliability, scalability, and developer productivity. - Identify opportunities to simplify architecture, reduce operational toil, and improve data platform leverage across teams. - Operate with a strong bias toward action and iterative delivery, moving quickly from problem definition to implementation and improvement. - Help raise the bar on engineering quality through thoughtful design, testing, documentation, and operational discipline. Qualifications - 5-8+ years of experience building and operating production-grade data pipelines and data systems. - Prior experience in mortgage, servicing, or similarly regulated financial domains. - Strong experience with industry-standard tools and platforms for ETL/ELT, orchestration, data warehousing, streaming, and BI. - Experience working with both OLTP and OLAP systems, with a strong understanding of the tradeoffs between transactional and analytical workloads. - Experience building flexible data pipelines that integrate with many different source and destination types, including databases, APIs, files, message queues, SaaS platforms, and event streams. - Experience supporting both batch and real-time data processing patterns. - Experience deploying and operating data infrastructure on major cloud platforms such as AWS, GCP, or Azure. - Strong SQL skills and experience with data modeling, transformation frameworks, and performance optimization. - Experience building AI-powered capabilities on top of LLMs, including orchestration, evaluation, and data integration patterns. - Experience with modern programming languages commonly used in data engineering, such as Python, Java, Scala, or Go. - Comfort working with CI/CD, infrastructure-as-code, observability, and production operations for data systems. - Strong judgment in ambiguous environments where requirements evolve and systems must balance speed, reliability, and flexibility. - Clear communication skills with both technical and non-technical teammates. Preferred Experience - Experience with modern orchestration and transformation tools such as Airflow, Dagster, dbt, or similar platforms. - Experience with cloud-native data warehouses or lakehouse platforms such as Snowflake, BigQuery, Redshift, Databricks, or equivalent technologies. - Experience with streaming and real-time data platforms such as Kafka, Kinesis, SQS, or similar systems. - Experience enabling BI and self-service analytics through curated datasets, semantic layers, and reporting platforms such as Looker, Power BI, Tableau, or similar tools. - Experience working with loan-level or transaction-heavy financial data within residential mortgage servicing domains. - Experience dealing with data reconciliation challenges across multiple systems, particularly where cash balances, or investor/reporting outputs must align. - Experience building data platforms that support AI, machine learning, or decisioning workflows. - Experience improving data quality, reliability, cost efficiency, and platform scalability as a system grows. A Note to Candidates If you are a strong data engineer with solid technical judgment, a systems mindset, and excitement for solving complex data problems, we would love to hear from you. If your background does not line up perfectly with every bullet, but this role feels like the kind of work you want to do, please apply. Bayview is an Equal Employment Opportunity employer. All aspects of consideration for employment and employment with the Company are governed on the basis of merit, competence and qualifications without regard to race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, or any other category protected by federal, state, or local law. #LI-Remote