Job Summary
The Data Engineer designs, builds, and optimizes data pipelines and warehouses that enable trusted analytics and AI-driven decision-making. This role supports the technical execution of large-scale system migrations, with a focus on data engineering infrastructure and additional efforts such as project coordination, data validation, stakeholder alignment, and operational readiness. The role collaborates with business stakeholders, data scientists, data architects, analysts, and engineers to ensure data quality, security, scalability, and effective delivery across platforms.

Key Responsibilities
• Design and develop scalable ETL/ELT pipelines and data integration practices.
• Architect end-to-end data pipelines, define standards for performance and reusability, and promote data engineering best practices.
• Implement and maintain data models, including star, snowflake, and data vault models, to support analytics and reporting.
• Establish data modeling standards, validate scalability and consistency across systems, and align data structures with enterprise architecture.
• Collaborate with data architects, analysts, and platform engineers to deploy and maintain data solutions in AWS, Azure, and GCP environments.
• Define deployment standards, automate infrastructure using Infrastructure as Code, and ensure platform performance.
• Ensure data quality, integrity, and security through validation, monitoring, and governance practices.
• Define data governance frameworks, quality SLAs, and metrics for production monitoring.
• Contribute to continuous improvement and automation of data engineering processes.
• Lead process improvement initiatives and establish data engineering best practices across teams.
• Support analytics, data science, and reporting teams with accessible, well-documented, and high-quality data.
• Establish metadata strategies and support governance and compliance standards.
• Support large-scale migration initiatives, including data validation, technical coordination, stakeholder alignment, cutover planning, and go-live execution.
• Lead technical decision-making and guide solution design across teams and workstreams.
• Lead delivery planning, technical design, and execution across multiple workstreams using Agile or hybrid delivery models.
• Collaborate with stakeholders to support change management and ensure operational readiness for new platforms.
• Mentor and provide technical guidance to junior and mid-level engineers.
• Support full lifecycle project delivery, including requirements, design, development, testing, cutover planning, and production implementation.

Required Qualifications
• 8-12 years of experience engineering enterprise-scale data solutions for Senior-level roles, or 12+ years of experience architecting multi-cloud data platforms for Lead-level roles.
• Experience designing and implementing end-to-end data architectures and scalable data platforms.
• Experience leading cross-functional teams and establishing engineering best practices across projects.
• Advanced SQL and data modeling experience, including enterprise-scale design, performance optimization, and scalability.
• Strong Python skills for developing scalable, reusable data pipeline frameworks and automation.
• Deep expertise in ETL/ELT architecture and orchestration using technologies such as Airflow, dbt, Dataflow, or Informatica.
• Strong understanding of cloud data platforms such as Snowflake, BigQuery, Redshift, Databricks, and Azure Fabric.
• Experience with multi-cloud data architectures.
• Ability to architect end-to-end data pipelines and define standards for performance, scalability, and reusability.
• Experience establishing data modeling standards and aligning data structures with enterprise architecture.
• Strong knowledge of Infrastructure as Code using Terraform or similar tools and CI/CD for data platform deployments.
• Experience defining and implementing data quality frameworks, SLAs, and monitoring strategies.
• Knowledge of data governance frameworks, including metadata management, lineage, access controls, and compliance.
• Experience mentoring and developing junior and mid-level engineers.
• Ability to drive process improvements and establish engineering best practices across teams.
• Experience in the mortgage industry, including ABF and mortgage servicing processes such as settlements, remittances/cash consolidation, and servicing workflows.
• Good communication and change management skills, with the ability to align stakeholders and ensure operational readiness.
• Experience leading full lifecycle projects, including requirements, design, development, testing, cutover planning, and go-live execution.
• Experience with Agile or hybrid project delivery models.
• Experience with tools such as Snowflake, BigQuery, dbt, Airflow, Python, Git, Terraform or similar tools, and Tableau or Power BI.

Preferred Qualifications
• Experience architecting multi-cloud data platforms and governing enterprise data engineering standards.
• Experience with Snowflake SnowPro, AWS Data Analytics Specialty, Azure Data Engineer Associate, or GCP Professional Data Engineer certifications.