Lead Data Architect

Job Title: Lead Data Architect

Location: Los Angeles, CA

Duration: Contract to Hire

Location Requirement: Job Summary

Client is seeking for a Datawarehouse Lead of large-scale data solutions. You will join a team delivering a transformative next-gen cloud hosted data platform.

The ideal candidate will have a proven track record in implementing next-gen cloud data solutions and transformation pipelines for large scale organizations. We are seeking someone with deep technical skills in a variety of data warehouse related technologies to play an important role in developing and delivering early proofs of concept and production implementation. The successful candidate will have hands-on experience in a multitude of data domains; including building data warehouse models, ETL development, automated testing framework, performance tuning, service delivery, incident resolution tracking, and change management.

Key Responsibilities

• Architecting and maintaining Operational data stores (ODS), Enterprise Data Warehouse (EDW) and associated data intelligence environments including data lakes, data marts, and metadata repositories.
• Designing and implementing high performance data movement pipelines using multiple ETL tools (Informatica, Azure Data Factory, SQL Packages, etc) with batch and real-time event data streams.
• Developing scalable and re-usable frameworks for ingestion of structured and semi-structured data; and implementing various large-scale settlement and reconciliation frameworks.
• Designing and creating data architecture and data flow diagrams and collaborating with peers to translate requirements into logical and physical data models.
• Ensures data warehouse features and capabilities are incorporated into data model designs to optimize performance, resiliency, and scalability.
• Delivering and presenting proofs of concept to of key technology components to project stakeholders and internal technology leaders.
• Leading and coordinating the design and operationalization of Data Governance capabilities like data lineage, data catalog, data quality, etc.
• Problem-solving of Data warehouse and ODS application issues and production errors, including high level critical production issues that require immediate attention.
• Write, test and produce production grade ETL data pipeline code in financial services without relying on testing by other groups.
• Implement intelligent testing frameworks that auto-generate test cases, predict bugs, and enable self-healing.
• Leads the creation of test strategy, test planning, and test execution related to all aspects of testing like functional, integration, systems, end-to-end testing, accessibility, database validations, and automation.
• Integrate test suites into CI/CD pipelines (Jenkins, Github).

Required Qualifications

• Bachelors and/or masters degree in computer science, Computer Engineering or related field preferred.
• 8+ years of overall software development experience is required primarily with Data warehouse applications, with 3+ years in a technical leadership role.
• 3+ years of hands-on development experience in Cloud-based data warehouse platform (AWS Redshift preferred) and experience with ETL tools (Informatica Cloud preferred).
• 3+ years of development experience with Python scripting experience.
• Strong knowledge and experience in OLAP data modeling & design.
• Knowledge of data governance, security, and compliance standards (e.g., SOC 2, PCI DSS).
• Demonstrated expertise in performance tuning in various DB environments with large volumes of data.
• Familiarity with CI/CD pipelines (e.g., Jenkins, GitHub Actions) and infrastructure-as-code tools.
• Expertise in understanding complex business needs, analyzing, designing, and developing solutions.
• Communication and professional skills and the ability to navigate relationships across business units.

Preferred Qualifications

• Experience in fintech or enterprise data warehouse platforms.
• Familiarity with data warehouse operational domains such as partner reporting, compliance analytics, and business intelligence.
• Contributions to open-source QA frameworks or technical publications.
• Demonstrated leadership in building scalable QA practices, as outlined in internal QA leadership guidance.