01Overview
Note: The job is a remote job and is open to candidates in USA. Community Loan Servicing, LLC is seeking a Data Engineer specializing in Mortgage Servicing. This role is crucial for building and evolving data systems that support analytics, reporting, and AI development, while ensuring data integrity and operational efficiency.ResponsibilitiesDesign, 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 databasesDevelop scalable ETL and ELT workflows for both batch and real-time processingEnsure pipelines are reliable, testable, observable, and easy to extend as business needs evolveBuild reusable data integration patterns that support growing volumes, new source systems, and downstream consumers across analytics, applications, and AI initiativesDesign and manage data architectures that support OLTP, OLAP, and reporting workloads across operational and analytical environmentsBuild and optimize data models, warehouse schemas, and curated datasets for analytics and BI use casesContribute to the design and operation of modern data platforms, including warehouses, lakehouses, streaming systems, and supporting orchestration frameworksHelp define patterns for data storage, partitioning, performance optimization, retention, and lifecycle managementDesign and maintain data models that accurately reflect loan-level lifecycle events, including payment activity, balances, adjustments, and status changesEnsure consistency and reconciliation across systems where transactional, financial, and reporting data must alignIdentify and resolve discrepancies across source systems, and build data structures that support accurate, auditable outputs for downstream operational processes, reporting, and decisioningDeploy, operate, and improve data pipelines and data stores on major cloud platforms such as AWS, GCP, or AzureUse infrastructure-as-code, CI/CD, and automation practices to improve deployment speed, consistency, and reliabilityMonitor production data systems using logging, alerting, and observability tooling to proactively identify and resolve issuesSupport secure, resilient, and cost-conscious operation of cloud-based data infrastructureImplement data quality checks, validation rules, reconciliation processes, and monitoring to ensure trustworthy data across systemsEstablish and maintain standards for lineage, documentation, metadata, schema evolution, and operational runbooksPartner with stakeholders to improve data accessibility, consistency, and usability while maintaining appropriate controls and governanceContribute to practices that support security, privacy, auditability, and compliance in a regulated environmentPartner closely with Product, Engineering, and business stakeholders to understand data needs, workflows, and constraintsTranslate business and operational requirements into clean, scalable, and maintainable data solutionsSupport downstream consumers of data, including analysts, researchers, product teams, and operational usersCommunicate clearly with both technical and non-technical stakeholders about data availability, quality, tradeoffs, and delivery timelinesContinuously improve pipeline performance, reliability, scalability, and developer productivityIdentify opportunities to simplify architecture, reduce operational toil, and improve data platform leverage across teamsOperate with a strong bias toward action and iterative delivery, moving quickly from problem definition to implementation and improvementHelp raise the bar on engineering quality through thoughtful design, testing, documentation, and operational disciplineSkills5-8+ years of experience building and operating production-grade data pipelines and data systemsPrior experience in mortgage, servicing, or similarly regulated financial domainsStrong experience with industry-standard tools and platforms for ETL/ELT, orchestration, data warehousing, streaming, and BIExperience working with both OLTP and OLAP systems, with a strong understanding of the tradeoffs between transactional and analytical workloadsExperience building flexible data pipelines that integrate with many different source and destination types, including databases, APIs, files, message queues, SaaS platforms, and event streamsExperience supporting both batch and real-time data processing patternsExperience deploying and operating data infrastructure on major cloud platforms such as AWS, GCP, or AzureStrong SQL skills and experience with data modeling, transformation frameworks, and performance optimizationExperience building AI-powered capabilities on top of LLMs, including orchestration, evaluation, and data integration patternsExperience with modern programming languages commonly used in data engineering, such as Python, Java, Scala, or GoComfort working with CI/CD, infrastructure-as-code, observability, and production operations for data systemsStrong .
