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Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and support scalable, secure data pipelines and cloud-based data solutions (AWS, Databricks) that enable reliable trading analytics.
Ensure data quality, lineage, observability, and operational documentation while optimizing performance, cost, and resilience of pipelines.
Use approved AI-enabled engineering tools responsibly to improve code quality and productivity within an Agile delivery environment.
Minimum Requirements
Minimum 3+ years of enterprise data engineering experience.
Strong Python (including Pandas, HTTP/API integration), advanced SQL, and hands-on experience with Apache Airflow and Databricks (Spark/PySpark, Delta Lake).
Experience building and operating data solutions on AWS services (S3, IAM, Glue, CloudWatch); familiarity with EKS and Kafka is a plus.
Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or related field, or equivalent practical experience.
Ideal Candidate Profile
Experienced in developing large-scale ETL/ELT pipelines for data-intensive, regulated domains such as trading, financial services, or energy.
Comfortable working in Agile environments using tools like Azure DevOps, with strong operational discipline for code versioning, CI/CD, testing, and documentation.
Skilled in leveraging AI tools for practical engineering tasks with a good understanding of AI fundamentals, prompt design, and responsible AI use.