As a Staff Software Engineer, you will help move beyond traditional monolithic SQL engines and batch processing systems. You will create the next generation of distributed data storage and processing platforms—systems that can scale without limits and outperform conventional query engines, while offering clean, simple, and expressive data interfaces. These interfaces will support a wide range of users, including our web application, business analytics teams, and artificial intelligence systems.

Primary Duties:
• Design and implement scalable, high‑performance solutions.
• Collaborate across disciplines to guide product direction and execution.
• Establish strong foundations for code structure and quality.
• Mentor and support other engineers.
• Define and maintain engineering processes that ensure high‑quality output.

Minimum Qualifications:
• Bachelor’s degree (or higher) in Computer Science, Engineering, or a related field.
• 8+ years of hands‑on engineering experience building highly scalable systems.
• 4+ years of experience serving as a trusted technical decision‑maker, balancing short‑ and long‑term business goals.
• 4+ years of experience using SQL or similar query languages with large, multi‑table datasets.
• Background in architecting, developing, and deploying large distributed systems.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Experience building CI/CD pipelines.
• Strong knowledge of server-side technologies such as Java, Python, Scala, C#, C++, or Go.

Preferred KSAs:
• Strong expertise with tools like Apache Spark, SQL, and Python for data processing and analysis.
• Familiarity with data technologies and architectures such as event‑driven systems, distributed computing, and in‑memory processing.
• Experience with SQL and NoSQL databases (e.g., MySQL, PostgreSQL, Cassandra, MongoDB), with a focus on high‑performance analytics.
• Experience designing, deploying, and managing data warehouses such as Snowflake or Amazon Redshift.
• Understanding of partitioning, sharding, and indexing for high‑load environments.
• Ability to design data models that support analytical use cases efficiently.
• Knowledge of data pipeline architectures, including ETL/ELT, batch processing, and real‑time streaming.
• Skill in optimizing data pipelines for scale, speed, and efficiency.
• Ability to apply caching and indexing techniques to reduce processing and query time.
• Knowledge of orchestration tools such as Apache Airflow, AWS Glue, and Apache Kafka.
• Understanding of data security principles and regulatory compliance (e.g., GDPR, HIPAA) through sound data governance.

Physical Requirements:
• Extended periods of sitting and computer use. Some walking and occasional lifting may be required.