Data Analyst II, Tech

About the Role

We are seeking a highly motivated and technically adept Senior Data Analyst to join our Data Engineering and Sharing Solutions Team, specializing in Ethics Compliance & Security data. In this high-impact role, you will be the technical bridge between complex data ecosystems and global regulatory requirements. You will assist in architecting data-sharing solutions and defining the rigorous SQL logic and Data Quality (DQ) frameworks that ensure our safety data is accurate, compliant, and audit-ready. To succeed, you must balance deep-dive technical work with the ability to consult cross-functionally, translating ambiguous legal needs into scalable analytical products.

What the Candidate Will Do

1. Define Reporting Logic: Develop and refine the core SQL transformations and business logic that serve as the "single source of truth" for all compliance and safety datasets.
2. Define Data Quality (DQ) Standards: Establish and implement automated DQ frameworks and validation scripts to ensure data integrity before it reaches external regulators.
3. Assist in Solution Architecture: Collaborate on the design of data-sharing frameworks that allow Operations teams to fulfill law enforcement and regulatory requests at scale.
4. Technical Consultation: Serve as the primary analytical point of contact for Legal and Public Policy teams, translating complex regulatory requirements into precise technical specifications.
6. Drive Scalability: Identify patterns in recurring data requests and build the technical documentation and modular workflows required to automate fulfillment.

What the Candidate Will Need

1. 4+ years of experience in data analytics, with a focus on logic building and data modeling. B.Tech/B.E. or equivalent degree in a quantitative field (Statistics, Computer Science, etc.) and background in Compliance, Risk, or Public Safety within FinTech, InsurTech, or a similar high-stakes environment.
2. Advanced SQL Mastery: Expert-level ability to query multi-layered databases (CTEs, Window Functions) and build optimized data transformations.
3. Data Quality Expertise: Proven track record of defining validation rules, exception reporting, and audit-ready datasets.
4. Analytical Solution Design: Experience designing "self-service" data products or modular logic that enables other teams to execute with autonomy.
5. Privacy Mindset: Deep understanding of data privacy (PII handling) and the technical nuances of data redaction.