Job summary
Job overview
This job is for a DevOps Engineer at Swish Analytics, focusing on developing and maintaining predictive sports analytics data products in a remote environment.
Responsibilities and impact
The role involves developing and managing Kubernetes clusters, collaborating with Data Science and Engineering teams, implementing best practices for deployment, and responding to system incidents.
Compensation and benefits
The base salary ranges from $130,000 to $185,000, with equal opportunity employment practices in place.
Experience and skills
Candidates should have 4+ years of experience in DevOps, AWS, CI/CD pipelines, and containerization tools, along with 2+ years of Python or Go experience; preferred qualifications include ML Ops experience and knowledge of advanced statistical methods.
Work environment and culture
Swish Analytics emphasizes a team-oriented environment with a passion for accuracy and predictive data, suitable for those comfortable in fast-paced and evolving settings.
Company information
Swish Analytics is a startup specializing in sports analytics, betting, and fantasy, aiming to innovate predictive sports analytics data products.
Job location and travel
This position is 100% remote.
Company overview
Swish Analytics is a leading sports analytics and technology company that specializes in providing data-driven solutions for sports betting, daily fantasy sports, and fan engagement. They generate revenue through subscription-based services, licensing their predictive analytics software, and offering consulting services to sports organizations and media companies. Founded in 2014, Swish Analytics has rapidly grown by leveraging advanced machine learning algorithms and real-time data to deliver accurate predictions and insights, making them a trusted partner in the sports industry.
How to land this job
Tailor your resume to highlight your experience with Kubernetes, AWS, and CI/CD tools, as these are critical for the DevOps Engineer role at Swish Analytics.
Emphasize your ability to work collaboratively with Data Science and Data Engineering teams, showcasing any past experiences that demonstrate your teamwork and communication skills.
Apply through multiple platforms such as the Swish Analytics corporate site and LinkedIn to maximize your exposure for this position.
Connect with professionals in the DevOps division at Swish Analytics on LinkedIn to gain insights about the role and company culture; potential ice breakers could include discussing recent industry trends or asking about their experiences with specific technologies mentioned in the job description.
Optimize your resume for ATS by incorporating keywords from the job description, such as 'Kubernetes,' 'AWS,' 'CI/CD,' and 'machine learning pipelines' to improve your chances of passing initial screenings.
Consider using Jennie Johnson's Power Apply feature, which can streamline your application process by tailoring your resume, identifying the best application channels, and suggesting relevant LinkedIn connections to network with, allowing you to focus more on your job search strategy.
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