Senior Staff Engineer, Data
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Strong ghost-posting signals
- open for 436 days (90+ without a fill is a strong ghost signal)
- reposted 1× (reposts correlate with ghost postings)
- removed from the board and reposted at least once
- 52 open roles at this company in 30 days (mass-hiring blitz)
- no salary disclosed (correlates with ghost postings)
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About the role
What You'll Do
Data Platform Architecture: Lead the design of scalable and reliable data pipelines, storage solutions, and access layers using modern cloud-native technologies.
Foundational Systems: Own and evolve the core data platform components (e.g., event streaming, lakehouse architecture, batch & streaming ETL, data cataloging).
Data Governance: Define best practices for data quality, lineage, privacy, and access control to ensure regulatory compliance and trust in the data.
Cross-functional Collaboration: Work closely with Product, Analytics, Infrastructure, and Security teams to align data platform capabilities with organizational goals.
Technical Leadership: Mentor engineers across the organization, establish coding and architectural standards, and influence the strategic direction of the platform.
Innovation & Modernization: Evaluate and integrate emerging technologies that improve performance, observability, developer experience, and cost efficiency.
What You'll Bring
10+ years of software engineering experience with deep expertise in data infrastructure, distributed systems, or backend platform engineering.
Proven track record of designing and delivering production-grade data platforms at scale (e.g., supporting hundreds of TBs of data, thousands of jobs/day).
Strong experience with tools like Snowflake, dbt, Kafka, Airflow, Spark, and cloud-native technologies (e.g., AWS, GCP, or Azure).
Hands-on experience building APIs and services for data access, metadata management, and platform observability.
Familiarity with data privacy regulations (GDPR, SOC2, HIPAA) and enterprise-grade security practices.
Effective communicator and collaborator who can influence engineering and non-engineering audiences alike.
Experience in a startup or high-growth SaaS environment.
Exposure to AI/ML data pipelines or real-time analytics platforms.
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