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.

 

·      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)

 

·      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

 

·      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

 

·      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

 

·      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

 

·      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

 

·      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

 

·      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