Data Software Engineer Staff

• Design, build, and maintain event-driven distributed data infrastructure and data applications.
• Develop and optimize robust ETL/ELT pipelines to support batch and real-time data workflows.
• Integrate and normalize diverse data sources, ensuring high standards of data quality, accuracy, and consistency.
• Lead the design and implementation of real-time data processing systems for analytics, operational intelligence, and reporting use cases.
• Collaborate cross-functionally with data scientists, ML engineers, and product teams to design end-to-end data solutions.
• Establish best practices for data engineering, including testing, monitoring, deployment automation, and observability.
• Mentor engineers and contribute to architectural decisions, technical strategy, and roadmap planning for the data platform.

Qualifications
 

• 10–16 years of proven experience in software engineering, with a focus on data infrastructure and engineering.
• Expertise in object-oriented programming, design patterns, algorithm optimization, and problem-solving from first principles.
• Strong experience working with unstructured log data, real-time data processing, distributed computing frameworks, and streaming data frameworks.
• Proficient in Python; experience with Java or Scala is a plus.
• Deep expertise in data engineering technologies, including ETL/ELT pipelines, data integration, and operational monitoring.
• Experience drafting proofs of concept (POCs) and collaborating cross-functionally to develop prototypes and production-ready solutions.
  • Proficiency with AWS services (Lake, Glue, EMR) and Airflow