Sr. Data Engineer - Architecture

Job description

As the Sr. Data Engineer - Architecture, you will drive data infrastructure that enables data-informed decision-making, applying modern engineering and distributed systems practices. You will partner closely with Product Managers, Data Analysts, Software Engineers, and business stakeholders to deliver stable, high-quality data pipelines, enterprise reporting, and datasets that support analytics and machine learning use cases. A primary responsibility of this role is to optimize and maintain our OLTP databases, ensuring reliability, performance, and production readiness. This includes auditing systems, improving data quality, refactoring legacy pipelines, and applying best practices for schema design, indexing, and query performance. In parallel, you will build and maintain scalable data systems that power advanced analytics across the business. You will design performance testing and release validation frameworks to prevent regressions and ensure data integrity, while establishing strong production processes such as monitoring, alerting, backup and recovery, access controls, and incident response. ### What You Need (Required Knowledge, Skills & Abilities): ### Education & Experience - Bachelor's degree in Mathematics, Statistics, Computer Science, or related field - 5+ years of experience as a Database Engineer, Data Engineer, or similar role ### Core Data Engineering & Architecture - Experience designing, implementing, and maintaining high performant, scalable OLTP systems. - Hands-on experience and advanced knowledge of SQL (e.g., Postgres, Snowflake) - Strong experience with data modeling, data warehouses, and lakehouse architectures - Experience designing and implementing scalable data architectures, including batch and streaming pipelines - Experience building ELT pipelines with dbt and Snowflake - Intermediate to advanced Python development skills ### Database Optimization & Reliability - Experience assessing and improving existing database systems, including performance tuning (indexing, query optimization, partitioning) and data quality remediation - Strong understanding of database internals and transactional systems - Experience implementing backup, recovery, and high-availability strategies ### Performance Testing & Release Validation - Experience designing and implementing performance/load testing frameworks for data systems - Knowledge of benchmarking, regression testing, and release validation processes - Experience building automated testing pipelines to ensure data quality and system performance across deployments ### Production Operations & Data Reliability - Experience defining and maintaining production database processes, including monitoring, alerting, and incident response - Familiarity with observability tools and practices (logging, metrics, tracing) - Strong understanding of SLAs, SLOs, and data reliability best practices ### Tools & Platforms - Experience with AWS data technologies (Glue, Kinesis, Lambda) - Experience with orchestration tools (Airflow) - Experience with infrastructure-as-code (Terraform) - Knowledge of the Software Development Lifecycle ### Preferred Skills & Experience: - Experience with CI/CD pipelines, especially for data systems - Experience with containerization (Docker, Kubernetes) - Knowledge of encryption, anonymization, and tokenization - Experience with open table formats and data catalogs - Familiarity with data observability tools (e.g., Monte Carlo, Datadog, Prometheus) ### Who You Are (Soft Skills): - Detail-oriented, with a strong data quality mindset - Strong problem-solving and troubleshooting skills with a proactive approach to system reliability - Self-starter with a bias toward ownership and continuous improvement - Comfortable bringing structure and best practices to ambiguous or legacy environments - Thrives in a fast-paced, startup-oriented, team-focused culture - Positive, collaborative, and energetic attitude - Excellent verbal and written communication skills - Ability to clearly explain complex technical issues to both technical and non-technical audiences