Software Engineer, Data Reliability Platform

San Francisco, CA - USA

Monthly Salary: Not Disclosed

Posted on: 5 days ago

Vacancies: 1 Vacancy

Job Summary

Block is one company built from many blocks all united by the same purpose of economic empowerment. The blocks that form our foundational teams People Finance Counsel Hardware Information Security Platform Infrastructure Engineering and more provide support and guidance at the corporate level. They work across business groups and around the globe spanning time zones and disciplines to develop inclusive People policies forecast finances give legal counsel safeguard systems nurture new initiatives and more. Every challenge creates possibilities and we need different perspectives to see them all. Bring yours to Block.

The Role

The Data Reliability team ensures that Blocks data ecosystem is observable trustworthy and resilient. We build the systems that detect prevent and mitigate data issues across critical analytics and ML workloads. Sitting within our AI and Data/Analytics (AiDA) organization we partner closely with data infrastructure Machine Learning and product analytics teams to provide foundations for reliable data from lineage and quality signals to data incident response and service-level metrics.

You Will

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.

You Have

4 years of experience in data data platform or software engineering roles.
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.
A collaborative mindset and a focus on making systems observable self-healing and transparent.

Technologies We Use and Teach

Languages: Kotlin Python SQL
Data Platforms: Snowflake Databricks Kafka
Workflow & Orchestration: Airflow Prefect
Infrastructure: Kubernetes AWS Terraform

Were working to build a more inclusive economy where our customers have equal access to opportunity and we strive to live by these same values in building our workplace. Block is an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class. We will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and fair chance ordinances. We believe in being fair and are committed to an inclusive interview experience including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter who will treat these requests as confidentially as possible.Want to learn more about what were doing to build a workplace that is fair and square Check out ourID page.

While there is no specific deadline to apply for this role U.S. roles are typically open for an average of 55 days before being filled by a successful candidate. Please refer to the date listed at the top of this job page for when this role was first posted.

The Role

You Will

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.

You Have

4 years of experience in data data platform or software engineering roles.
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.
A collaborative mindset and a focus on making systems observable self-healing and transparent.