Data Engineer

Data Architecture Design: Design, implement, and optimize scalable data architectures, including data lakes, data warehouses, and streaming platforms, to support current and future analytical and operational needs.

End-to-End Data Pipeline Development: Build, test, and maintain complex ETL/ELT pipelines for both batch and real-time data processing, ensuring data accuracy, consistency, and reliability from source to consumption.

Technology Evaluation & Implementation: Research, evaluate, and integrate new data technologies, tools, and frameworks (e.g., big data processing engines, cloud services, orchestration tools) to enhance our data platform capabilities.

Performance Optimization: Continuously monitor and optimize data pipeline performance, scalability, and cost efficiency, ensuring efficient data flow and delivery at scale.

Data Modeling & Storage: Implement effective data models and manage various data storage solutions (e.g., relational databases, NoSQL databases, object storage).

Data Governance & Security: Implement and enforce data quality checks, validation rules, and security measures to ensure data integrity, privacy, and compliance with organizational policies.

Cross-Functional Collaboration: Work closely with data architects, data modeler, BI Specialists, data analysts and business stakeholders to understand data requirements, provide technical guidance, and deliver data solutions that align with business objectives.

Must be a U.S. person as defined under U.S. export regulations.

Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field from an accredited institution.

Minimum three years of professional experience in a Data Engineer or similar role, with a strong focus on building scalable data platforms.

Strong proficiency in SQL and expertise in at least one major programming language for data engineering (e.g., Python, Java, Scala). Python is highly preferred.