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