Zigsaw
11 - 50 employees
Founded 2016
Making Job-search and talent discovery simpler, faster and effective for Job-seekers.
Staff Data Engineer
Job not on LinkedIn
🕒 April 20
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Zigsaw
11 - 50 employees
Founded 2016
Making Job-search and talent discovery simpler, faster and effective for Job-seekers.
📋 Description
• Design and maintain a scalable identity resolution platform • Build pipelines and services to ingest, normalize, link, and version identity data across multiple sources • Ensure deterministic and probabilistic matching logic that is transparent, auditable, and measurable • Partner with product and analytics teams to expose identity data through reliable, well-documented APIs and datasets • Build and operate batch and streaming pipelines using modern data stack tools • Create clear documentation, standards, and runbooks for identity and governance systems • Own data governance foundations including data lineage, quality checks, schema enforcement, and access controls • Implement privacy-by-design principles (PII handling, consent enforcement, retention policies) • Collaborate with legal, privacy, and security teams to operationalize regulatory requirements (e.g., GDPR, CCPA) • Establish monitoring and alerting for data quality, freshness, and integrity
🎯 Requirements
• Production data engineering experience • Bachelor’s degree in computer science, related field or equivalent experience • Proficiency in Spark and Scala, with proven experience building data infrastructure in Spark using Scala • Experience in delivering significant technical initiatives and building reliable, large scale services • Experience in delivering APIs backed by relationship-heavy datasets • Experience implementing data governance practices, including data quality, metadata management, and access controls • Strong understanding of privacy-by-design principles and handling of sensitive or regulated data • Familiarity with data lakes, cloud warehouses, and storage formats • Strong proficiency in AWS services • Excellent written and verbal communication skills • Successful design and implementation of scalable and efficient data infrastructure • High attention to detail in implementation of automated data quality checks • Effective collaboration with cross-functional teams • Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs • Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review) • High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables
🏖️ Benefits
• Health insurance • Equity opportunities • Flexible work arrangements • Professional development
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