This role focuses on enabling front-office, advisor, and trading operations through low-latency data pipelines, scalable architectures, and governed data platforms. You will work closely with trading desks, portfolio management, and digital platforms to deliver reliable, compliant, and high-throughput data solutions.
Key Responsibilities
Trading Data Platform Engineering
· Design and build real-time and batch data pipelines supporting trading workflows (orders, executions, positions, market data)
· Develop low-latency data processing systems for near real-time decisioning
· Build scalable data architectures for high-volume transaction data
· Enable event-driven architectures using streaming platforms (Kafka, Kinesis)
Wealth Management & Trading Integration
· Integrate with trading platforms (OMS/EMS), portfolio systems, and advisor platforms
· Support use cases such as:
· Trade lifecycle tracking (order execution settlement)
· Portfolio performance and analytics
· Advisor dashboards and client reporting
· Ensure data consistency across front-, middle-, and back-office systems
Data Engineering & Architecture
· Build and manage data lakes / lakehouse architectures (Delta Lake, Iceberg, etc.)
· Develop ETL/ELT pipelines using modern frameworks
· Design data models optimized for trading and analytics workloads
· Implement API-driven data access layers for downstream consumption
Performance, Scalability & Reliability
· Optimize pipelines for low latency, high throughput, and fault tolerance
· Implement data quality, reconciliation, and observability frameworks
· Ensure high availability and disaster recovery for critical trading data systems
Governance, Risk & Compliance
· Implement data governance, lineage, and auditability
· Ensure compliance with regulatory requirements (SEC, FINRA, etc.)
· Enable data security, entitlements, and access controls
· Support trade surveillance and reporting requirements
Collaboration & Delivery
· Partner with trading desks, product teams, and architects to translate requirements into scalable data solutions
· Work closely with AI/analytics teams to enable downstream insights and models
· Mentor junior engineers and contribute to data engineering best practices
Required Qualifications
· 7–12+ years of experience in data engineering or backend engineering
· Strong expertise in:
· Python / Scala / Java
· SQL and distributed data processing (Spark, Flink, etc.)
· Hands-on experience with:
· Streaming platforms (Kafka, Kinesis, Pulsar)
· Data lake / warehouse technologies (Snowflake, Databricks, Redshift)
· Experience building real-time or near real-time data pipelines
· Strong understanding of data modeling and large-scale distributed systems
Preferred Qualifications
· Experience in Wealth Management or Capital Markets trading systems
· Familiarity with OMS/EMS platforms (e.g., Charles River Development, Aladdin, FIS)
· Knowledge of market data (equities, fixed income, derivatives) and trade lifecycle / post-trade processing
· Experience with cloud-native data platforms (AWS, Azure, GCP)
· Exposure to real-time analytics and risk systems