This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description The Senior Data Engineer will be responsible for designing and implementing data solutions that support the organization’s goals. - Develop and maintain data pipelines and ETL processes - Collaborate with cross-functional teams to understand data needs - Ensure data quality and integrity - Optimize data storage and retrieval processes - Provide technical guidance and mentorship to junior engineers Qualifications - Bachelor's degree in Computer Science, Engineering, or related field - 5+ years of experience in data engineering or related field - Strong programming skills in Python, Java, or Scala - Experience with SQL and NoSQL databases - Familiarity with data warehousing solutions Requirements - Proficiency in data modeling and database design - Experience with cloud platforms such as AWS or Azure - Strong analytical and problem-solving skills - Ability to work independently and in a team environment Benefits - Comprehensive health insurance - 401(k) with company match - Flexible work hours and telecommuting options - Opportunities for professional development and growth

• Architect and scale systems for AI-driven valuation tools • Lead development of data ingestion pipelines and workflows • Collaborate with backend engineers and product leadership

• Assess the current (“as-is”) data platform and define the future-state architecture in collaboration with the Big Data Engineering leadership • Perform gap analysis and contribute to the implementation of the future-state data platform • Work closely with internal stakeholders including team leads, enterprise architecture, data governance, and data DevOps teams • Draft and review big data architectures alongside senior engineering and BI leadership to ensure optimal technical solutions • Support refinement sessions by contributing accurate effort sizing and architectural input • Provide guidance to avoid silos by ensuring architectural decisions are inclusive and scalable across teams • Contribute to project planning through architectural insight and risk awareness • Promote best practices across the Big Data Engineering team, ensuring data accuracy, validity, reusability, and consistent coding standards • Stay up to date with emerging Big Data technologies and trends, communicating their value clearly to both technical and non-technical audiences • Support collective peer review practices and improve overall architectural quality

• Develop a Modern Azure Data Platform: Design, build, and operate end-to-end data solutions using Azure Data Factory, Azure Data Lake Storage Gen2, Databricks, and Azure Synapse Analytics. • Create Data Pipelines: Develop and maintain scalable ETL/ELT pipelines using PySpark and Spark, with a strong focus on data quality, reliability, and performance. • Multi-Tenant & Environment Support: Support and operate multi-tenant data platforms across multiple environments (development, test, production) with clear separation and governance. • Infrastructure & Platform Operations: Provision, configure, and maintain Azure data infrastructure, ensuring stability, security, and scalability. • CI/CD for Data Platforms: Build and maintain CI/CD pipelines for data pipelines and Databricks workloads, enabling automated deployments across environments. • Cost-Efficient & Best-Practice Azure Usage: Apply Azure best practices to optimize performance and cost, including resource sizing, lifecycle management, and cost monitoring. • Collaboration with BI & Data Teams: Work closely with BI and data teams to support efficient data models and reporting solutions. • Data Governance & Security Basics: Support data governance requirements such as access control, secure data handling, and basic metadata management.