{"schemaVersion":"jobsearcher.job.v1","id":"9f5cedd73cc3a857043be2b0","url":"https://jobsearcher.com/jobs/9f5cedd73cc3a857043be2b0","canonicalUrl":"https://jobsearcher.com/jobs/9f5cedd73cc3a857043be2b0","title":"Senior Data Architect (Azure Databricks/Python)","description":"Position Summary The Senior Data Architect is responsible for designing, developing, and maintaining robust, scalable, and high-performance enterprise data architectures within a modern cloud environment. This role requires deep expertise in Big Data solutions (Delta Lake architecture), modern data warehouse practices and operations, semantic layering, dimensional modeling (star schemas), transactional OLTP databases (3NF modeling), and advanced data modeling techniques. The ideal candidate will have at least 15 years of data modeling experience specifically within Business Intelligence and Analytics contexts, extensive hands-on experience with batch and streaming data processing, and strong expertise with Apache Spark, Databricks, and Spark Structured Streaming. Required skills include proficiency in Python programming, Azure cloud technologies, semantic modeling, and modern CI/CD deployment practices. Experience in ML engineering is highly desirable. The candidate must be able to collaborate quickly and effectively with data and engineering teams, clearly document source-to-target mappings, and reverse engineer existing database objects such as stored procedures, views, and complex SQL queries.\r\nResponsibilities Lead architecture design and implementation of enterprise-scale data platforms leveraging Databricks, Delta Lake, Azure cloud, and modern Big Data technologies.\r\nDesign, build, and maintain modern data warehouse solutions using dimensional modeling (star schema) and semantic layering to optimize analytics and reporting capabilities.\r\nDefine and enforce data modeling standards, guidelines, and best practices within analytics and BI contexts.\r\nArchitect robust batch processing and real-time streaming solutions using Apache Spark, Databricks, Kafka, Kinesis, and Spark Structured Streaming.\r\nEffectively collaborate with engineering teams to rapidly deliver data architecture solutions and support agile development practices.\r\nProvide clear, comprehensive source-to-target documentation, data lineage mappings, and semantic layer definitions.\r\nReverse engineer existing database structures, including stored procedures, views, and complex SQL logic, to document existing data processes and support modernization initiatives.\r\nProvide technical leadership, mentoring, and guidance to data engineering teams, ensuring alignment with architectural standards and best practices.\r\nEvaluate and continuously improve existing data architectures, optimize performance, and recommend enhancements for efficiency and scalability.\r\nCollaborate closely with stakeholders to define long-term data strategies and clearly communicate architectural decisions.\r\nEnsure compliance with industry standards, data governance practices, regulatory requirements, and security guidelines.\r\nChampion modern DevOps and CI/CD practices for data and analytics pipelines.\r\nRequirements Expert working knowledge of the data platform landscape along with best practices with respect to Relational databases, NoSQL databases, and \"Big Data\" architectures\r\nMinimum of 15 years of hands-on data modeling experience specifically in Business Intelligence (BI) and Analytics contexts.\r\nExtensive experience designing and implementing modern data architectures, including Big Data solutions (Delta Lake), modern data warehouses (star schema/dimensional modeling), semantic layering, and transactional OLTP (3NF) data modeling.\r\nPrior experience designing and building a Delta Lake using Medallion Architecture\r\nDeep understanding of relational and dimensional modeling, normalization (3NF), semantic modeling, and transactional database design principles.\r\nProven ability to produce detailed source-to-target mappings, data lineage documentation, semantic definitions, and reverse engineer existing stored procedures, views, and SQL logic.\r\nDemonstrated expertise in Apache Spark, Databricks, and Spark Structured Streaming for batch and real-time data processing.\r\nProficiency in Python programming required.\r\nStrong experience with Azure cloud technologies, including Azure Data Factory, Azure Storage, Azure Databricks, and related data services.\r\nSolid experience designing streaming data solutions using Kafka, Kinesis, or similar streaming technologies.\r\nKnowledge and hands-on experience implementing modern CI/CD practices for data engineering and analytics solutions.\r\nStrong analytical, organizational, and communication skills, with the ability to clearly articulate complex technical concepts to diverse stakeholders.\r\nProven ability to collaborate effectively and efficiently with engineering teams and business stakeholders.\r\nPreferred Qualifications ML Engineering experience or exposure to ML pipelines and model deployment processes highly desirable.\r\nExperience in the mortgage or financial services industry preferred but not required.\r\nExposure to FiveTran and Dynamics CRM.\r\nWhy work for #teamloanDepot loanDepot (NYSE: LDI) is a digital commerce company committed to serving its customers throughout the home ownership journey. Since its launch in 2010, loanDepot has revolutionized the mortgage industry with a digital-first approach that makes it easier, faster, and less stressful to purchase or refinance a home. Today, loanDepot enables customers to achieve the American dream of homeownership through a broad suite of lending and real estate services that simplify one of life's most complex transactions. With headquarters in Southern California and offices nationwide, loanDepot is committed to serving the communities in which its team lives and works through a variety of local, regional, and national philanthropic efforts.\r\nBase pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay for this roles is between $186,000 and $210,000 per year. Your base pay will depend on multiple individualized factors, including your job-related knowledge/skills, qualifications, experience, and market location.\r\nWe are an equal opportunity employer and value diversity in our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.\r\nJ-18808-Ljbffr","company":"LoanDepot","rawCompany":"loandepot","city":"Plano","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-04-09T08:05:28.141Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Data Architect (Azure Databricks/Python)","description":"Position Summary The Senior Data Architect is responsible for designing, developing, and maintaining robust, scalable, and high-performance enterprise data architectures within a modern cloud environment. This role requires deep expertise in Big Data solutions (Delta Lake architecture), modern data warehouse practices and operations, semantic layering, dimensional modeling (star schemas), transactional OLTP databases (3NF modeling), and advanced data modeling techniques. The ideal candidate will have at least 15 years of data modeling experience specifically within Business Intelligence and Analytics contexts, extensive hands-on experience with batch and streaming data processing, and strong expertise with Apache Spark, Databricks, and Spark Structured Streaming. Required skills include proficiency in Python programming, Azure cloud technologies, semantic modeling, and modern CI/CD deployment practices. Experience in ML engineering is highly desirable. The candidate must be able to collaborate quickly and effectively with data and engineering teams, clearly document source-to-target mappings, and reverse engineer existing database objects such as stored procedures, views, and complex SQL queries.\r\nResponsibilities Lead architecture design and implementation of enterprise-scale data platforms leveraging Databricks, Delta Lake, Azure cloud, and modern Big Data technologies.\r\nDesign, build, and maintain modern data warehouse solutions using dimensional modeling (star schema) and semantic layering to optimize analytics and reporting capabilities.\r\nDefine and enforce data modeling standards, guidelines, and best practices within analytics and BI contexts.\r\nArchitect robust batch processing and real-time streaming solutions using Apache Spark, Databricks, Kafka, Kinesis, and Spark Structured Streaming.\r\nEffectively collaborate with engineering teams to rapidly deliver data architecture solutions and support agile development practices.\r\nProvide clear, comprehensive source-to-target documentation, data lineage mappings, and semantic layer definitions.\r\nReverse engineer existing database structures, including stored procedures, views, and complex SQL logic, to document existing data processes and support modernization initiatives.\r\nProvide technical leadership, mentoring, and guidance to data engineering teams, ensuring alignment with architectural standards and best practices.\r\nEvaluate and continuously improve existing data architectures, optimize performance, and recommend enhancements for efficiency and scalability.\r\nCollaborate closely with stakeholders to define long-term data strategies and clearly communicate architectural decisions.\r\nEnsure compliance with industry standards, data governance practices, regulatory requirements, and security guidelines.\r\nChampion modern DevOps and CI/CD practices for data and analytics pipelines.\r\nRequirements Expert working knowledge of the data platform landscape along with best practices with respect to Relational databases, NoSQL databases, and \"Big Data\" architectures\r\nMinimum of 15 years of hands-on data modeling experience specifically in Business Intelligence (BI) and Analytics contexts.\r\nExtensive experience designing and implementing modern data architectures, including Big Data solutions (Delta Lake), modern data warehouses (star schema/dimensional modeling), semantic layering, and transactional OLTP (3NF) data modeling.\r\nPrior experience designing and building a Delta Lake using Medallion Architecture\r\nDeep understanding of relational and dimensional modeling, normalization (3NF), semantic modeling, and transactional database design principles.\r\nProven ability to produce detailed source-to-target mappings, data lineage documentation, semantic definitions, and reverse engineer existing stored procedures, views, and SQL logic.\r\nDemonstrated expertise in Apache Spark, Databricks, and Spark Structured Streaming for batch and real-time data processing.\r\nProficiency in Python programming required.\r\nStrong experience with Azure cloud technologies, including Azure Data Factory, Azure Storage, Azure Databricks, and related data services.\r\nSolid experience designing streaming data solutions using Kafka, Kinesis, or similar streaming technologies.\r\nKnowledge and hands-on experience implementing modern CI/CD practices for data engineering and analytics solutions.\r\nStrong analytical, organizational, and communication skills, with the ability to clearly articulate complex technical concepts to diverse stakeholders.\r\nProven ability to collaborate effectively and efficiently with engineering teams and business stakeholders.\r\nPreferred Qualifications ML Engineering experience or exposure to ML pipelines and model deployment processes highly desirable.\r\nExperience in the mortgage or financial services industry preferred but not required.\r\nExposure to FiveTran and Dynamics CRM.\r\nWhy work for #teamloanDepot loanDepot (NYSE: LDI) is a digital commerce company committed to serving its customers throughout the home ownership journey. Since its launch in 2010, loanDepot has revolutionized the mortgage industry with a digital-first approach that makes it easier, faster, and less stressful to purchase or refinance a home. Today, loanDepot enables customers to achieve the American dream of homeownership through a broad suite of lending and real estate services that simplify one of life's most complex transactions. With headquarters in Southern California and offices nationwide, loanDepot is committed to serving the communities in which its team lives and works through a variety of local, regional, and national philanthropic efforts.\r\nBase pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay for this roles is between $186,000 and $210,000 per year. Your base pay will depend on multiple individualized factors, including your job-related knowledge/skills, qualifications, experience, and market location.\r\nWe are an equal opportunity employer and value diversity in our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.\r\nJ-18808-Ljbffr","datePosted":"2026-04-09T08:05:28.141Z","dateModified":"2026-04-09T08:05:28.141Z","hiringOrganization":{"@type":"Organization","name":"LoanDepot","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Plano","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"9f5cedd73cc3a857043be2b0"},"url":"https://jobsearcher.com/jobs/9f5cedd73cc3a857043be2b0"}}