We are seeking an accomplished Data Engineer to join our rapidly growing team. This role is responsible for designing, building, and evolving scalable data pipeline architecture to ensure reliable, high-quality data delivery across the organization. The ideal candidate is a hands-on engineer with strong experience building and maintaining data pipelines, and a passion for delivering robust data solutions that enable analytics and business decision-making. The Data Engineer will partner with data architects, data analysts, data scientists, and cross-functional stakeholders to deliver trusted data assets supporting a wide range of business initiatives. They will ensure efficient and reliable data delivery across multiple teams, systems, and products in a dynamic environment. This role offers the opportunity to evolve and enhance a modern data platform by improving existing pipelines or redesigning them for greater scalability, performance, and maintainability. The successful candidate will apply modern software engineering practices, including AI-assisted development tools, to improve productivity, code quality, and delivery speed while maintaining strong engineering standards. RESPONSIBILITIES • Design, develop, and maintain scalable data pipelines and data products for internal and external consumers. • Build and optimize batch and near real-time data ingestion, transformation, and delivery processes. • Integrate data from internal and external sources to support business, reporting, and analytics requirements. • Collaborate with data architects, analysts, data scientists, and business stakeholders to deliver scalable data solutions and support Sisense dashboards and analytics assets. • Design and implement data models that support reporting, analytics, and operational use cases. • Ensure data quality, reliability, and performance through monitoring, validation, automated testing, and troubleshooting. • Write maintainable, well-documented, and testable code; participate in code reviews; and leverage AI-assisted development tools to improve quality and efficiency. • Support CI/CD, infrastructure automation, technical documentation, and continuous improvements to data architecture, tooling, and engineering practices QUALIFICATIONS • 2–4 years of professional experience in Data Engineering, Data Warehousing, or related roles. • Strong hands-on experience with Python and SQL for building scalable data pipelines and transformation logic. • Experience with Apache Spark, Parquet, and Azure Databricks, including Databricks workflows, Delta Lake, Delta Sharing, and Unity Catalog. • Strong SQL expertise including performance tuning, indexing, partitioning, query optimization, and stored procedure development. • Solid understanding of ETL/ELT methodologies, data warehousing principles, and modern data engineering best practices. • Experience designing and implementing data models to support analytics, reporting, and operational use cases. • Experience supporting or working with BI tools such as Sisense (or similar platforms). • Experience with CI/CD pipelines and version control practices (e.g., GitLab, Jenkins, or equivalent). • Experience working in fast-paced product environments with an emphasis on delivery, maintainability, and minimizing technical debt. • Strong communication skills with the ability to collaborate across technical and non-technical stakeholders BONUS QUALIFICATIONS • Experience building lightweight data applications or internal tools using any of the following frameworks such as Streamlit, Dash, Flask, Gradio, Shiny, or Node.js. • Ability to navigate ambiguity, prioritize effectively, and adapt to changing business needs. • Prior experience in financial services or regulated environments is a plus