Sr. AI Platform Data Engineer, Ring Decision Science
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Senior Data Engineer Job Postings
Senior Data Engineer job postings offer opportunities for professionals with experience in big data projects. This position targets individuals skilled in database management, data processing, and analytics infrastructure development. Job descriptions usually include clear requirements for technical skills in data engineering, software development, and cloud technologies.
What Does a Senior Data Engineer Do?
A Senior Data Engineer is responsible for designing, managing, and improving data infrastructures. This role involves managing data flows, processing large datasets, and preparing them for analysis. Data engineers typically optimize ETL (Extract, Transform, Load) processes, develop data warehouse solutions, and implement strategies to improve data management system performance. They also integrate new technologies to make data processing more efficient.
What Details Should You Pay Attention to When Reviewing Job Postings?
When reviewing Senior Data Engineer postings, pay attention to the data processing tools and technologies required. Candidates are often expected to be proficient in big data tools such as Hadoop, Spark, and Kafka. Experience with SQL, NoSQL, and other database management systems is also important. Additionally, requirements related to cloud platforms (AWS, Google Cloud, Azure) and data engineering methodologies should be carefully examined.
Which Education and Certifications Are Required?
A bachelor’s degree in computer engineering, software engineering, data science, or a related field is typically required. Advanced certifications in big data technologies and cloud infrastructure, such as Google Cloud Professional Data Engineer or AWS Certified Big Data – Specialty, provide an advantage. Experience in data engineering, machine learning, and big data applications is also an important criterion.
Which Traits Are Employers Looking For?
For a Senior Data Engineer, strong analytical thinking, problem-solving skills, and leadership abilities are essential. This role requires a combination of technical expertise and managerial skills, including project management and team leadership. Data engineers should be adaptable to rapidly changing technologies and capable of solving complex data problems. Strong communication skills are also required, as they often collaborate closely with data scientists and other teams.
What Are the Job Opportunities for a Senior Data Engineer?
Senior Data Engineers have job opportunities across various sectors. Technology firms, e-commerce companies, financial services, healthcare, and telecommunications are among the highest-demand industries. Additionally, emerging fields such as cloud computing and artificial intelligence increasingly require data engineers. They can also work in consulting firms or on independent projects. Career advancement opportunities exist for specialization in software development, data science, and other areas, leading to higher-level roles.
How Can I Apply?
When applying for Senior Data Engineer positions, it is important to highlight your experience in data engineering, the technologies you have used, and relevant projects. Demonstrating proficiency in data processing and analytics tools (e.g., Hadoop, Spark, Kafka) will make your application stand out. Supporting your experience with examples of software and database management work can strengthen your application. Tailoring your application to the specific requirements of the job posting is also highly recommended.
In Which Fields Can a Senior Data Engineer Work?
Senior Data Engineers can work in big data engineering, data science, artificial intelligence, machine learning, cloud computing, and data visualization. This role is particularly in demand in software development, financial services, healthcare, and e-commerce sectors to develop data infrastructures. They may also work on data warehouse design, ETL processes, database management, and API integration.
Which Programming Languages Are Required?
Applications for Senior Data Engineer positions generally require proficiency in programming languages such as Python, Java, Scala, and SQL. Knowledge of data processing and analytics tools like Apache Hadoop, Apache Spark, and Apache Kafka is also important. Database management expertise, including SQL and NoSQL databases (e.g., MongoDB, Cassandra), may be required. Experience with cloud computing platforms (AWS, Azure, Google Cloud) and big data management tools provides an additional advantage.
Sr. AI Platform Data Engineer, Ring Decision Science
26.11.2025
Kanada
Deneyim: 6-8 Yıl
Type of Work: Tam Zamanlı
Work Location: Uzaktan
Department Level: Uzman
Job Description
Description
We seek an AI Platform Builder—a Data Engineer focused on developing Platforms and Agentic AI solution—who embraces prompt-driven development with strong technical, analytical, communication, and stakeholder management skills. This role sits at the intersection of data engineering, business intelligence, and platform engineering—requiring partnership with software development engineers, scientists, data analysts, and business stakeholders across various verticals. You will design, evangelize, and implement platform features and curated datasets that power Artificial Intelligence/Machine Learning (AI/ML) initiatives and self-service analytics, helping us provide a great neighbor experience at greater velocity.
This role requires a first-principles approach to leveraging AI at every layer of the data stack—from using AI agents to write and optimize code, to building AI-powered platforms that serve AI models, to deploying intelligent agents that make data accessible. You will use AI to build AI infrastructure, automate the automation, and create self-improving systems that continuously enhance data quality, discoverability, and usability.
Key job responsibilities
You will build and maintain efficient, scalable, and privacy/security-compliant data pipelines, curated datasets for AI/ML consumption, and AI-native self-service data platforms using an AI-first development methodology. As a trusted technical partner to business stakeholders and data science teams, you'll deliver well-modeled, easily discoverable data optimized for specific use cases while leveraging AI-powered solutions and agentic frameworks to build continuously improving systems.
A day in the life
- Lead AI-assisted stakeholder engagement sessions across verticals like Subscriptions, Security, Sales, and Marketing
- Design and build curated datasets leveraging AI code generation and Agentic AI tools
- Build and maintain data pipelines using AI-assisted development with AWS services and internal Amazon tools
This Role will:
- Implement AI-powered self-service platforms with natural language interfaces
- Create intelligent governance systems for data classification, PII detection, and lineage tracking
- Facilitate AI-augmented workshops for stakeholders to explore data capabilities collaboratively
About the team
The Analytics & Science team for Decision Sciences is at the forefront of Ring's transformation into an AI-powered organization. We address cross-organizational data models, develop governance frameworks, provide direct Business Intelligence (BI) support across multiple teams, and build customer-facing and internal AI tools that fundamentally improve how effectively and quickly the organization makes decisions.
Basic Qualifications
- 5+ years of data engineering experience
- Experience with data modeling, warehousing and building Extract, Transform, and Load (ETL) pipelines for both analytics and ML use cases
- Experience with Structured Query Language (SQL) and at least one programming language (Python, Java, Scala, or NodeJS)
- Experience building datasets or features for machine learning models or self-service analytics
- Extensive hands-on experience with Generative AI (GenAI) enhanced development pipelines, AI coding assistants, and prompt engineering
- Demonstrated track record of building AI agents, agentic workflows, or AI-powered automation tools
- Demonstrated ability to build tools, frameworks, or platforms that enable others
Preferred Qualifications
- Experience with AWS technologies like Bedrock, SageMaker, Redshift, Simple Storage Service (S3), AWS Glue, EMR, Athena, Kinesis, FireHose, Lambda, Step Functions, SageMaker Feature Store, and Identity and Access Management (IAM) roles and permissions
- Experience building multi-agent systems, LangChain/LangGraph applications, or custom AI agent frameworks
- Experience with prompt engineering, Retrieval-Augmented Generation (RAG) systems, and Large Language Model (LLM) fine-tuning
- Experience in at least one modern scripting or programming language with production-quality code standards
- Experience with non-relational databases / data stores
- Experience with BI tools (QuickSight, Tableau, Looker) and designing datasets for analytical consumption
- Experience building or contributing to AI-native self-service data platforms, feature stores, or intelligent data cataloging systems
- Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Candidate Criteria
Education Level: Lisans
Language: İngilizce
Military Service Status: Farketmez
University Department: All departments
Skills
About Company
About Amazon
Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. We are driven by the excitement of building technologies, inventing products, and providing services that change lives. We embrace new ways of doing things, make decisions quickly, and are not afraid to fail. We have the scope and capabilities of a large company, and the spirit and heart of a small one. Together, Amazonians research and develop new technologies from Amazon Web Services to Alexa on behalf of our customers: shoppers, sellers, content creators, and developers around the world. Our mission is to be Earth's most customer-centric company. Our actions, goals, projects, programs, and inventions begin and end with the customer top of mind. You'll also hear us say that at Amazon, it's always "Day 1." What do we mean? That our approach remains the same as it was on Amazon's very first day - to make smart, fast decisions, stay nimble, invent, and focus on delighting our customers.
