The ideal candidate has experience working with large datasets and distributed computing technologies. They relish working with large volumes of data, enjoy the challenge of highly complex technical contexts, and above all else are passionate about data and analytics. They are expert in data modelling, ETL design, and business intelligence tools, with hands-on knowledge of columnar databases such as Redshift and other related AWS technologies. They partner passionately with customers to identify strategic opportunities in the field of decision intelligence.
Beyond traditional BI expertise, our team operates heavily within the agentic and generative AI space. The ideal candidate brings genuine curiosity and a builder mindset towards Amazon's AI ecosystem:
- AI-assisted development: High proficiency using Kiro/Claude as daily development tools not just prompting, but leveraging AI to accelerate pipeline development, data analysis, and automation
- Skills & agents: Hands-on experience building AI skills and agents within the Amazon internal ecosystem (AIM, MCP integrations), or a strong desire to learn
- Amazon Q & Quick AI Features: Familiarity with surfacing data insights through AI-powered experiences and understanding how these tools augment BI workflows
- AgentCore/Strands Agents: Exposure to agentic architectures is a strong plus understanding how to orchestrate AI agents for complex data workflows
- Builder mentality: You don't just use AI tools, you extend them, create reusable components, and proactively look for opportunities to apply AI to solve business problems
They are a self-starter, comfortable with ambiguity, able to think big while paying careful attention to detail, and enjoy working in a fast-paced team that continuously learns and evolves on a day-to-day basis.
Basic qualifications
- Experience in analyzing and interpreting data with Redshift, Oracle, NoSQL etc.
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with one or more industry analytics visualization tools (e.g. Excel, Tableau, QuickSight, MicroStrategy, PowerBI) and statistical methods (e.g. t-test, Chi-squared)
- Experience with scripting language (e.g., Python, Java, or R)
- Experience with SQL
- Experience in the data/BI space
Preferred qualifications
- Master's degree, or Advanced technical degree
- Knowledge of data modeling and data pipeline design
- Experience with statistical analysis, co-relation analysis
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Senior Industrial Analytics Engineer
