Senior Data Engineer

• Design develop and maintain scalable ETL/ELT data pipelines.
  
• Build and optimize data warehouses data lakes and data models.
  
• Integrate data from multiple sources including APIs databases and third-party systems.
  
• Ensure data quality consistency security and governance.
  
• Monitor and troubleshoot data pipelines and resolve performance issues.
  
• Optimize SQL queries and database performance.
  
• Collaborate with cross-functional teams to understand data requirements and deliver solutions.
  

Requirements

Core Data Engineering & Architecture

• Design and build end-to-end data pipelines (ETL/ELT) for structured and unstructured data
  
• Develop scalable data platforms handling terabytes of data with high reliability and low latency
  
• Strong expertise in data modeling warehousing and lakehouse architectures
  
• Own data quality lineage governance and observability frameworks
  
• Hands-on experience with Spark Hadoop Kafka Flink (batch real-time processing)
  
• Build and optimize high-throughput distributed data systems
  
• Experience in streaming event-driven architectures for large-scale financial data
  
• Deep expertise in AWS / Azure / GCP (Data Lakes Warehouses Compute Storage)
  
• Tools: Databricks Snowflake Redshift Synapse BigQuery
  
• Pipeline orchestration using Airflow Prefect or similar frameworks
  
• Strong coding in Python SQL Scala
  
• Focus on performance optimization reliability and production-grade systems
  
• Experience with CI/CD DevOps and infrastructure-as-code
  
• Ability to design architectures and make technology decisions at scale
  
• Strong understanding of:
  
• Trade lifecycle transactions risk compliance regulatory reporting
  
• Customer 360 payments lending capital markets data and Wealth Management Business
  
• GenAI / LLM integration (RAG embeddings vector stores)
  
• Lead legacy-to-cloud data platform migrations
  
• Drive data re-engineering system decomposition and modernization initiatives
  
• Drive resilience failover and incident response frameworks
  
• Work across engineering data science risk compliance and product teams
  
• Ability to translate business needs (risk reporting revenue) into data solutions
  
• Mentor engineers and drive engineering best practices and standards
  

Benefits