Job Description

Job Description

Job Description

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

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