As a Data Reliability Engineer, you will be a key contributor to our data engineering team, focusing on the operational health and reliability of our data ecosystem. You will work closely with data engineers, data scientists, software engineers, and business stakeholders to identify potential data quality issues, develop preventative measures, and ensure our data infrastructure is resilient and performs optimally. Your work will directly impact our ability to make informed decisions and deliver reliable data products to our customers.

KEY RESPONSIBILITIES

• Data Quality Assurance: Develop and implement automated data quality checks, validation rules, and anomaly detection mechanisms to proactively identify and resolve data integrity issues.
• Monitoring & Alerting: Establish comprehensive monitoring and alerting systems for data pipelines and data quality metrics, ensuring timely detection and response to incidents.
• Incident Response & Root Cause Analysis: Lead efforts to troubleshoot, diagnose, and resolve data-related incidents, performing thorough root cause analysis and implementing preventative measures.
• Performance Optimization: Identify and address performance bottlenecks in data processing systems and databases, optimizing data flow and query efficiency.
• Documentation & Best Practices: Create and maintain detailed documentation for data pipelines, data models, and data reliability processes. Evangelize and enforce data reliability best practices across the organization.
• Collaboration: Partner with data engineers to build robust and scalable data infrastructure, with data scientists to understand their data needs, and with business users to ensure data accuracy for reporting and analytics.
• Tooling & Automation: Evaluate, recommend, and implement new tools and technologies to enhance data reliability, automation, and operational efficiency.
• Data Governance Support: Contribute to data governance initiatives, ensuring compliance with data policies and regulations.
• Work on any task or help solve problems where needed — be humble and scrappy!

WHAT YOU’LL NEED TO SUCCEED

• Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related quantitative field.
• 2+ years of experience in data engineering, data reliability engineering, or a similar role focused on data quality and operational excellence.
• Strong proficiency in SQL and experience with relational and/or NoSQL databases (e.g., PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, Cassandra, MongoDB).
• Familiarity with reliability platforms (e.g., DataDog, Splunk, Grafana, New Relic).
• Familiarity with RESTful APIs.
• Experience with data quality frameworks, tools, and methodologies.
• Willingness and ability to learn new software platforms as necessary.
• Excellent problem-solving, analytical, and critical thinking skills.
• Ability to work independently and collaboratively in a fast-paced environment.
• Strong communication and interpersonal skills.

WHAT WILL SET YOU APART

• Familiarity with Agile development practices.
• Experience in at least one scripting or programming language (e.g., Python, Scala, Java).
• Experience with data pipeline orchestration tools (e.g., Apache Airflow, Dagster, Prefect).
• Strong understanding of data warehousing concepts and data modeling principles.
• Experience with Linux based command line tools.
• Familiarity with cloud platforms and their data services (e.g., AWS S3, EC2, Glue, Kinesis; GCP Cloud Storage, Dataflow, Pub/Sub; Azure Data Lake, Data Factory).
• A knack for benchmarking and optimization.
• Experience working in an entrepreneurial or enterprise environment.