Data Engineering builds and manages the pipelines, applications and infrastructure for collecting, processing, and storing Nine’s data. We are responsible for collecting hundreds of millions of events from client devices each day and streaming them directly into our data warehouse. We extract data from the majority of Nine’s enterprise systems, centralise it in our data warehouse, and create rich data models for analysis and to inform business operations and strategy.

The team is also responsible for building solutions to surface data to users including dashboards, Slack integrations and automated static reports, as well as providing data feeds to downstream systems for personalising audience experiences and commercialisation.

• Design, develop and maintain data pipelines that extract, load and transform data from various sources into the data warehouses.

• Design and implement data models that align with business requirements and support efficient querying and analysis.

• Ensure the pipelines are reliable, scalable, and efficient, considering factors including data volume, velocity, and variety.

• Implement data quality checks and validation processes to maintain accurate and reliable data.

• Set up monitoring and alerting to proactively identify and resolve issues.

• Perform root cause analysis for incidents and implement corrective actions.

• Contribute to the technical design and vision for the data platform.

• Collaborate with cross-functional teams, including product managers, data scientists, analysts, and business stakeholders to understand data requirements and deliver solutions that meet business needs.

• Communicate technical concepts and solutions effectively to non-technical stakeholders.

• Stay updated with emerging data engineering technologies and trends, and evaluate their potential benefits for the organisation's data ecosystem.

About Us:

At Spinify, we're transforming how sales teams learn, grow, and succeed through cutting-edge AI-driven coaching and performance tools. We are on a mission to shift from gamification towards an intelligent coaching platform that leverages AI and real-time data to deliver personalized coaching, learning, and competition recommendations for sales teams. As we scale, we’re looking for an experienced AWS Machine Learning Engineer to help us build and maintain an innovative platform that ties coaching, learning, and AI together in a way that’s never been done before.

The Role:

We are seeking a Senior AWS Machine Learning Engineer who will be responsible for designing, developing, and deploying AI-powered solutions that personalize learning experiences, predict performance outcomes, and provide real-time coaching feedback. You will be working with a stack of cutting-edge AWS technologies to create an adaptive system that integrates real-time data, predictive analytics, and machine learning models.

Key Responsibilities:

• Build and deploy scalable Machine Learning models using AWS SageMaker for personalized learning, predictive analytics, and performance coaching.
• Develop and manage real-time data pipelines using AWS Kinesis and AWS Glue for streaming performance data, integrating third-party data sources, and ensuring accurate data flow.
• Design, implement, and optimize natural language querying using AWS Q to allow managers and users to interact with performance data and get actionable insights.
• Use Amazon Personalize to create dynamic, personalized learning paths and recommendations for users based on their performance and activity.
• Implement emotional intelligence analysis using Amazon Rekognition and Amazon Comprehend to analyze video coaching sessions and deliver AI-driven feedback on tone, sentiment, and body language.
• Build and maintain real-time dashboards using Amazon QuickSight for visualizing performance data, coaching progress, and competition standings.
• Create serverless workflows using AWS Lambda and API Gateway to automatically trigger coaching suggestions, performance alerts, and competition rule adjustments based on real-time data.
• Work with AWS Bedrock to integrate generative AI models for creating personalized quizzes, coaching strategies, and competition challenges.
• Ensure data privacy and security across all services, managing user access with AWS IAM and maintaining compliance with best practices (GDPR, SOC2, etc.).

Requirements:

• 5+ years of experience with AWS Cloud Services, particularly SageMaker, Kinesis, Glue, Lambda, Personalize, and QuickSight.
• Strong background in machine learning, including model training, optimization, and deployment using AWS SageMaker.
• Experience in building and managing real-time data pipelines using Kinesis and integrating large datasets using Glue or other ETL tools.
• Expertise in Natural Language Processing (NLP), with practical experience using Amazon Comprehend and Amazon Transcribe for sentiment and text analysis.
• Familiarity with AWS Bedrock and integrating generative AI models into real-world applications.
• Strong programming skills in Python and SQL.
• Experience with API development using API Gateway and Lambda for creating serverless, scalable APIs.
• Experience building real-time data visualizations and dashboards using Amazon QuickSight.
• Knowledge of data security best practices, including working with AWS IAM and compliance frameworks such as GDPR and SOC2.
• Strong analytical and problem-solving skills, with the ability to optimize performance and scale solutions.

Preferred Qualifications:

• Previous experience working in sales enablement or coaching platforms.
• Experience with DevOps tools such as CloudFormation or Terraform for automating infrastructure deployment and scaling.
• Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch) for advanced AI model building.

What We Offer:

• Competitive salary with performance-based bonuses.
• Remote work flexibility.
• Opportunity to work with cutting-edge AI and cloud technologies.
• A collaborative and innovative team environment.
• Opportunities for professional development and growth in the AI and ML space.