Data Analytics Engineer – Nearshore

Job Description:
Our partner, a global elevator manufacturer, is looking for a driven Data Analytics Engineer to join their team focused on solving high-impact problems like identifying revenue leakage, targeting the right customers, and enabling double-digit service growth! You will have the opportunity to be on the front edge of how AI is applied inside an enterprise, enabling capabilities like natural language interaction with data and dramatically faster idea-to-execution cycles. The environment is fast, agile, and outcome-driven, making it especially compelling for someone who wants ownership, autonomy, and the ability to directly impact business growth through data. This isn’t a traditional “build a dashboard from requirements” role, it’s an opportunity to sit at the center of real business decisions and use data to drive what happens next!

Requirements:
• 4+ years of experience in Data Analytics Engineering, building and maintaining scalable, production-grade data pipelines and end-to-end data workflows (ingestion → transformation → consumption)
• Deep expertise in Power BI, including advanced data modeling, complex DAX, performance tuning, and delivering executive-level dashboards and self-service analytics solutions
• Strong hands-on experience with Snowflake, including data modeling (dimensional schemas), complex SQL development, and query/cost performance optimization in a cloud data warehouse
• Advanced SQL skills and experience with modern data transformation practices/tools (e.g., dbt or similar), with a focus on building modular, scalable, and maintainable data models
• Exposure to cloud data ecosystems (AWS/Azure/GCP) and AI/LLM-enabled analytics including an understanding of structuring data for semantic layers, self-service BI

Nice to have:
• Experience with Sigma Computing or similar self-service analytics tools
• Experience in service-based or field operations analytics (pricing, margins, customer targeting, etc.)
• Experience working in agile, product-driven environments with rapid iteration cycles

Responsibilities:
• Design, build, and optimize data models and pipelines that make enterprise data AI-ready and easily consumable
• Translate ambiguous business problems into data-driven insights and solutions (without relying solely on predefined requirements)
• Rapidly prototype and iterate on analytical solutions, dashboards, and data experiences using tools like Power BI and Sigma
• Enable “talk to your data” capabilities by structuring datasets for AI/LLM-based querying and insights
• Partner with business stakeholders to identify opportunities for revenue growth, cost optimization, and operational efficiency
• Deliver insights in days—not weeks—through agile, sprint-based execution
• Ensure data quality, trust, and consistency across multiple data sources and regions
• Continuously explore and leverage AI tools (e.g., Copilot, LLMs) to accelerate development and insight generation