Block's Data Reliability team needs to ensure the trustworthiness and resilience of the company's data ecosystem, building systems to detect, prevent, and mitigate data issues across critical analytics and ML workloads.
Requirements
- Strong proficiency in Python or Java/Kotlin, and SQL for analytical and diagnostic workflows (or a willingness to learn!)
- Familiarity with data pipeline orchestration, alerting, or monitoring systems (e.g., Airflow, Dagster, or Prefect).
- Familiarity with incident management practices, including on-call participation, postmortems, and root cause analysis.
- Familiarity with modern data warehousing and streaming systems such as Snowflake, Databricks, Kafka, or similar.
- Understanding of data modeling, lineage, and metadata concepts and their role in data trust and compliance.
Responsibilities
- Develop and scale data quality and observability integrations.
- Partner with platform, analytics, and ML teams to identify, diagnose, and prevent recurring data issues.
- Contribute to the company-wide reliability strategy, designing core metrics and standards for data health.
- Collaborate with data platform and privacy teams to improve metadata completeness and accountability.
- Share knowledge and help establish engineering best practices for testing, observability, and operational excellence.
Other
- 4+ years of experience in data, data platform, or software engineering roles.
- A collaborative mindset and a focus on making systems observable, self-healing, and transparent.
