Senior Data Engineer - Capital Markets (R13923)

Role overview A senior-level data engineering position focused on the capital markets domain is open for an experienced builder who can own complex data platforms end to end. The role centers on designing scalable architectures, leading multi-month initiatives, and partnering with stakeholders to translate business needs into robust data solutions. It combines hands-on technical work with mentorship, technical leadership, and a strong emphasis on using AI-assisted tools to improve productivity.

Responsibilities - Design and implement scalable, reliable data architectures that support both operational and analytical workloads. - Build and maintain data pipelines, ETL processes, and integrations handling large volumes of structured and unstructured data. - Optimize data pipelines, warehouses, and lakes for performance, scalability, security, and reliability. - Establish data quality standards, validation rules, documentation, and governance practices across the platform. - Lead architectural decisions, code reviews, and troubleshooting efforts, including root-cause analysis of production incidents. - Mentor junior engineers, coordinate cross-functional work, and communicate progress, risks, and dependencies to stakeholders.

Requirements - 8+ years of experience in data engineering, with deep exposure to data architecture, ETL, and database management. - Strong programming skills in Python or PySpark, plus working knowledge of Java or Scala. - Proven experience building complex end-to-end data pipelines and integration solutions. - Strong SQL skills and familiarity with both relational and NoSQL database technologies. - Hands-on experience with big data platforms such as Spark, Kafka, or Hadoop, and orchestration tools like Airflow, Databricks, or Jenkins. - Experience operating on AWS, Azure, or GCP and working in Agile environments (Scrum, Lean, or Kanban), with clear communication across technical and non-technical teams.

Nice to have - Bachelor's or Master's degree in Computer Science, Data Science, or a related field. - Databricks experience or certification, and familiarity with cloud data services such as AWS Redshift, S3, or Azure SQL Data Warehouse. - Experience implementing data governance, data quality, or observability frameworks. - Prior leadership of cross-functional, multi-month engineering initiatives. - Practical use of AI-assisted engineering tools for development, troubleshooting, documentation, or productivity gains.

Benefits and work setup - Remote-friendly arrangement. - Country-specific benefits package that may include major medical insurance, savings fund, life insurance, internet and electricity allowance, and paid parental leave; eligibility and plan details vary by location and employment status.