Senior Data Engineer

About the Role We are looking for an experienced Data Engineer to design, build, and optimize scalable data platforms that support analytics, reporting, machine learning, and business decision-making. You will work closely with cross-functional teams including Data Science, Product, and Engineering to deliver reliable, high-quality data solutions in a large-scale, data-driven environment. Key Responsibilities Design, build, and maintain scalable data pipelines and ETL/ELT workflows. Develop and optimize data models, warehouse structures, and large-scale datasets for performance and efficiency. Partner with Data Scientists, Product Managers, and Software Engineers to understand business requirements and deliver data solutions. Ensure data quality through monitoring, validation, alerting, and anomaly detection processes. Support data governance, privacy compliance, and data lifecycle management initiatives. Improve reliability, scalability, and operational excellence across the data platform. Drive technical projects independently from requirements gathering through implementation and delivery. Requirements Minimum 5 years of experience in Data Engineering, Data Platform Engineering, or a related field. Strong SQL expertise and experience working with large-scale data warehouses and distributed data systems. Proficiency in Python and/or Java for data engineering and automation. Hands-on experience building and maintaining production-grade ETL/ELT pipelines. Strong understanding of data modeling concepts, including dimensional modeling and star/snowflake schemas. Experience with large-scale data processing frameworks such as Spark. Familiarity with workflow orchestration, data quality frameworks, and monitoring tools. Understanding of data governance, security, and privacy best practices. Strong stakeholder management and communication skills. Ability to work independently in a fast-paced and highly collaborative environment. Nice to Have Experience supporting machine learning or AI-driven data initiatives. Exposure to cloud-based data platforms and modern data architecture. Experience working with large-scale distributed systems and high-volume datasets. What the Day-to-Day Looks Like Building and optimizing data pipelines that power analytics and AI initiatives. Collaborating with product, engineering, and data science teams on new projects. Troubleshooting data issues and improving data reliability. Designing scalable data models and warehouse solutions. Monitoring data quality and implementing governance best practices. Top 3 Non-Negotiables Strong SQL expertise with large-scale data environments. Hands-on experience building production ETL/ELT pipelines using Python or Java. Experience with distributed data processing technologies such as Spark and modern data warehousing concepts. Initial 8-months contract with potentiality to extend.