Staff Software Engineer

• Be the technical lead for feature development in a team of 5-10 engineers and influencing the technical direction of the overall engineering organization.
• Decompose business objectives into valuable, incrementally releasable user features accurately estimating the effort to complete each.
• Contribute code to feature development efforts demonstrating to others efficient design, delivery and testing patterns and techniques.
• Strive for high quality outcomes, continuously look for ways to improve team productivity and product reliability, performance, and security.
• Develop the talents and abilities of peers and colleagues. Create a memorable legacy as you progress toward your personal and professional objectives.
• Foster your personal and professional development continually seeking assignments that challenge you.

Skills & Experience:

Successful Candidates must demonstrate an appropriate combination of:

• 6 - 10+ years of experience as a software engineer.
• 3+ years of experience as a Staff or lead software engineer.
• Bachelors degree in computer science or a STEM field.
• A portfolio of thought leadership and individual technical accomplishments.
• Full understanding of Agile software development methodologies and practices.
• Strong communication skills both verbal and written.

Extensive experience working with technologies and concepts such:

• Behavior-driven or test-driven development
• JVM-based languages such as Java and Scala
• Development frameworks such as Spring Boot
• Asynchronous programming concepts, including Event processing
• Database technologies such as SQL, Postgres/MySQL, AWS Aurora DBs, Redshift, Liquibase or Flyway
• No-SQL technologies such as Redis, MongoDB and Cassandra
• Streaming technologies such as Apache Kafka, Apache Spark or Amazon Kinesis
• Unit-testing frameworks such as jUnit
• Performance testing frameworks such as Gatling
• Architectural concepts such as micro-services and separation of concerns
• Expert knowledge of class-based, object-oriented programming and design patterns
• Development tools such as GitHub, Jira, Jenkins, Concourse, and Maven
• Cloud technologies such as AWS and Azure
• Data Center Operating Technologies such as Kubernetes, Apache Mesos Apache Aurora, and TerraForm and container services such as Docker and Kubernetes
• Monitoring and operational data analysis practices and tools such as DataDog, Splunk and EL