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

Role Description The Data Engineer on the Nebula team 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. - Working across data ingestion, transformation, storage, modeling, and delivery. - Partnering closely with Product, Engineering, AI, Analytics, and domain Subject Matter Experts (SMEs). - Translating complex business processes and data needs into production-ready data pipelines and platforms. - Contributing 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. - Operating effectively in a modern engineering environment using automation, observability, and infrastructure-as-code practices. - 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. - 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. - Data Platform & Storage: - Design and manage data architectures that support OLTP, OLAP, and reporting workloads. - 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. - Help define patterns for data storage, partitioning, performance optimization, retention, and lifecycle management. - Cloud Deployment & Operations: - Deploy, operate, and improve data pipelines and data stores on major cloud platforms. - 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. - 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. - Establish and maintain standards for lineage, documentation, metadata, schema evolution, and operational runbooks. - Partner with stakeholders to improve data accessibility, consistency, and usability. - Contribute to practices that support security, privacy, auditability, and compliance. - Cross-Functional Collaboration: - Partner closely with Product, Engineering, and business stakeholders to understand data needs. - 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. - Operate with a strong bias toward action and iterative delivery. - Help raise the bar on engineering quality through thoughtful design, testing, documentation, and operational discipline. Qualifications - 2-4+ years of experience building and operating production-grade data pipelines and data systems. - 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. - Experience building flexible data pipelines that integrate with many different source and destination types. - Experience supporting both batch and real-time data processing patterns. - Experience deploying and operating data infrastructure on major cloud platforms. - Strong SQL skills and experience with data modeling, transformation frameworks, and performance optimization. - Experience with modern programming languages commonly used in data engineering. - Comfort working with CI/CD, infrastructure-as-code, observability, and production operations for data systems. - Clear communication skills with both technical and non-technical teammates. Preferred Experience - Experience with modern orchestration and transformation tools. - Experience with cloud-native data warehouses or lakehouse platforms. - Experience with streaming and real-time data platforms. - Experience enabling BI and self-service analytics through curated datasets. - Experience in fintech, mortgage, lending, payments, insurance, or other regulated domains. - Experience building data platforms that support AI, machine learning, or decisioning workflows. Benefits - Competitive salary range of $220,000 to $300,000, plus an annual bonus. - Medical coverage starting on day one. - Company-matched 401(k). A note to candidates You do not need prior fintech or finance experience to succeed in this role. 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.

Role Description We are seeking a detail-oriented Google Sheets and Microsoft Excel expert to manually migrate and rebuild over 1,000 Google Sheets files into Excel while maintaining formulas, formatting, structure, and functionality. The ideal candidate will have advanced spreadsheet expertise, strong analytical skills, and experience working with complex formulas and large data sets. - Manually migrate Google Sheets documents to Microsoft Excel format. - Rebuild and validate complex formulas, functions, and spreadsheet logic in Excel. - Ensure data integrity, formatting consistency, and workbook functionality throughout the migration process. - Identify and resolve compatibility issues between Google Sheets and Excel. - Organize and manage large volumes of spreadsheet files efficiently. - Perform quality assurance testing to ensure accuracy after migration. - Collaborate with internal stakeholders to clarify spreadsheet structures and business logic when required. - Maintain confidentiality and security of company data. Qualifications - Advanced expertise in Google Sheets and Microsoft Excel. - Strong knowledge of: - Complex formulas and functions - Pivot tables - Lookup functions (VLOOKUP, XLOOKUP, INDEX/MATCH) - Conditional formatting - Data validation - Spreadsheet troubleshooting - Experience migrating spreadsheets between platforms. - Strong attention to detail and accuracy. - Ability to manage high-volume repetitive tasks efficiently. - Strong organizational and time management skills. - Experience working with large datasets is considered an asset. - Experience with macros, VBA, or spreadsheet automation. - Familiarity with Google Apps Script. - Data cleanup and normalization experience. - Previous migration or data conversion project experience. Company Description At MSP Corp, we don’t just build our own teams — we help our clients build theirs. As a national leader in managed IT services, we partner with exceptional companies to find the right talent to power their growth.