Lead Software Engineer
Company | SpyCloud |
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Location | Austin, TX, USA |
Salary | $Not Provided – $Not Provided |
Type | Full-Time |
Degrees | |
Experience Level | Senior |
Requirements
- 6+ years of experience in designing and delivering scalable data engineering solutions.
- Expertise in AWS Redshift for high-performance data processing and analytics.
- Hands-on experience with Amazon Bedrock and Generative AI technologies.
- Strong programming skills in Python, Java, or Scala.
- Deep knowledge of RESTful APIs, data modeling, and relational (SQL) + NoSQL databases.
- Experience with streaming data technologies such as Amazon Kinesis or Apache Kafka.
- Familiarity with open-source data processing frameworks like Apache Spark, Flink, or Storm.
- Strong grasp of software engineering best practices, including CI/CD, testing, and version control (GitHub, AWS CodeBuild, GitHub Actions).
- Deep expertise in AWS services: Redshift, API Gateway, Lambda, EMR, EC2, ECS.
- Experience with serverless architectures, containerization, and distributed data processing.
- Excellent problem-solving skills with a proactive approach to troubleshooting.
- Strong written and verbal communication skills for technical and non-technical audiences.
Responsibilities
- Design, develop, and deploy robust data pipelines, APIs, and backend services.
- Optimize and scale AWS Redshift for analytical workloads, ensuring high performance.
- Leverage Amazon Bedrock to build and integrate Generative AI-powered solutions.
- Ensure high-quality results and timely delivery of product roadmap milestones.
- Drive architectural decisions and design scalable data solutions.
- Provide technical guidance on best practices for AWS Bedrock, Redshift, Kinesis, Kafka, and Spark.
- Troubleshoot and resolve complex data-related issues.
- Foster a culture of technical excellence, innovation, and collaboration.
- Work closely with cross-functional teams to align data initiatives with business goals.
- Mentor and support junior engineers, fostering a culture of continuous learning.
- Identify opportunities to improve team processes and engineering standards.
Preferred Qualifications
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No preferred qualifications provided.