Senior Data Engineer, Product Data Systems

This is a fully remote position, open to applicants in Argentina.

📋 Description

• Design, develop, test, deploy, and manage production services for data ingestion, normalization, enrichment, identity resolution, scoring, and intelligence delivery.

• Create maintainable pipeline workers, event consumers, APIs, scheduled processes, and supporting libraries.

• Take ownership of the entire software lifecycle, encompassing architecture, implementation, testing, deployment, monitoring, incident response, and continuous improvement.

• Establish reusable engineering patterns for the team.

• Develop asynchronous workflows with clearly defined data, event, and work contracts.

• Design systems to handle duplicate delivery, ordering constraints, idempotency, retries, timeouts, partial failures, dead-letter handling, backpressure, and recovery.

• Ensure pipeline state and failures are observable, including reconciliation for missing, delayed, duplicated, or inconsistent processing.

• Facilitate safe replay and reprocessing without unintentionally altering the meaning of historical results.

• Maintain source evidence, provenance, lineage, processing context, and applicable versions.

• Define validation and quality controls at service boundaries and evolve schemas, contracts, and processing logic safely.

• Maintain tenant isolation while integrating shared intelligence with customer-private evidence, configuration, and conclusions.

• Implement authorization, retention, deletion, audit, GDPR, and SOC 2 requirements throughout data-processing workflows.

• Assess managed services, open-source components, existing capabilities, and purpose-built services based on product and operational needs.

• Contribute to architectural decisions through working software, written proposals, prototypes, and technical reviews.

• Collaborate with Product, Platform Engineering, Threat Research, Data Science, Security, and customer-facing teams.

• Convert product requirements into technical contracts, communicate trade-offs, and mentor engineers in modern data and distributed systems practices.

⛳️ Requirements

• Substantial hands-on experience in building and operating data-intensive software for a SaaS product used externally.

• Strong software engineering expertise in data systems.

• Experience in production development with Go, Scala, Rust, Java, or another comparable backend service language.

• Willingness to primarily use Go for developing pipeline and product data services.

• Proficiency in Python and SQL.

• Familiarity with relational, document, graph, key-value, analytical, and object-storage models and their trade-offs.

• Understanding of distributed systems concerns, including asynchronous processing, delivery semantics, concurrency, backpressure, idempotency, consistency, recovery, and failure isolation.

• Experience in designing or operating event-driven systems utilizing Kafka, Azure Event Hubs, AWS Kinesis, or similar messaging infrastructure.

• Proficient with containers, cloud infrastructure, automated testing, CI/CD, infrastructure automation, monitoring, and production operations.

• Ability to assess provenance, replay, data quality, multi-tenant isolation, shared data, private customer context, and authorization boundaries.

• Ability to evaluate unfamiliar technologies based on engineering principles, communicate effectively, constructively challenge weak assumptions, and take ownership of delivery outcomes.

• Relevant experience in cybersecurity, threat intelligence, fraud, abuse prevention, or other evidence-intensive domains is advantageous.

• Relevant experience with data provenance, explainability, auditability, or regulated data systems may be beneficial.

• Relevant experience with graph-based data processing, Neo4j, Azure Data Explorer, or Azure Data Lake Storage is a plus.

• Relevant experience with machine-learning feature pipelines, model inputs and outputs, or feedback and learning systems may be helpful.

• Relevant experience in migrating legacy batch or pipeline workloads into service-based, event-driven architectures is beneficial.

• Relevant experience in operating customer-facing data systems under established reliability and recovery expectations may assist.

• Must be authorized to work in the country of residence; this position does not qualify for visa sponsorship.

• Experience with every listed technology is not mandatory.

🏝️ Benefits

• Flexible time off.

• Sick days.

• All public holidays.

• Stock options.

• Location-independent work with a fully distributed team.

• Benefits customized to the country in which you will be working.

• Reasonable accommodations for qualified individuals with disabilities as necessary.