Software Development Engineer - Data Acquisition & Normailization

Key Responsibilities

• Build and maintain connectors to government registries, telcos, licensing authorities, and commercial data providers.
• Standardize and reconcile heterogeneous data formats into clean schemas usable by the Identity Trust Graph.
• Monitor and help resolve upstream source changes; contribute to retries, fallbacks, and error handling to improve pipeline reliability.
• Contribute to the Attribute Validation Service (AVS) by adding trusted data that validates identity attributes against sources of record.
• Help deliver clean and validated attribute data to downstream consumers including Wallet, Fraud, and Domains.
• Assist in reporting coverage and freshness metrics to Product, Ops, and Analytics stakeholders.
• Handle sensitive data in accordance with NIST, ISO 27001, and FedRAMP standards.
• Write high-quality, maintainable, and well-tested code, including automated tests and observability instrumentation.
• Participate in system design discussions, code reviews, and technical documentation to support team alignment.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent experience).
• 4+ years of experience developing web applications using OOP languages such as Java, Ruby, JavaScript, TypeScript, Go, Python, Rust, or C++.
• Exposure to data acquisition or integration work, including APIs, screen scraping, ETL, or normalization pipelines.
• Experience building systems in Docker, Kubernetes or Nomad and services in a containerized, cloud-based, infrastructure-as-code driven ecosystem such as GCP.
• Ability to deliver features end to end, including automated test coverage, observability, monitoring, and documentation.
• Ability to communicate technical tradeoffs clearly and work collaboratively within a team.
• Proficiency and strong interest in AI-assisted development tools (e.g., Claude Code or Codex) to accelerate delivery and code quality.

Preferred Qualifications

• Familiarity with operating data pipelines with reliability and SLA requirements.
• Understanding of distributed systems concepts, caching, asynchronous processing, and cloud-native patterns.
• Exposure to authentication and authorization standards (OAuth2, OIDC, JWT, or custom schemes).
• Familiarity with identity and credential verification systems, including data validation, proofing, or trust scoring.
• Exposure to event-driven architectures (Kafka, SNS/SQS) and patterns for decoupled service communication.
• Experience with cloud infrastructure (AWS, GCP, or Azure), including containerization and deployment pipelines.
• Familiarity with observability, monitoring, and incident response best practices.
• Awareness of compliance and security requirements for sensitive data (NIST, FedRAMP, ISO 27001).
• Bonus: Exposure to FinTech, identity, or data aggregation companies (e.g., Plaid, Yodlee, Envestnet).