Senior Data Engineer - Reference Data (Assistant Vice President)

Jefferies is looking for a highly experienced Senior Data Engineer to join the Reference Data Group within our Technology division. You will play a key role in designing, building, and managing the firm's critical reference data platforms — including Security Master , Account Master , and Counterparty Master — which underpin trading, risk, compliance, and operations across the firm.

This is a high\-impact, hands\-on engineering role. You will work closely with business stakeholders, data consumers, and cross\-functional technology teams to deliver robust, scalable, and well\-governed data pipelines and platforms on modern cloud infrastructure.

Reference Data at Jefferies is foundational — the data you build and manage powers trading systems, regulatory reporting, risk models, and client\-facing applications globally.

About the Team

The Reference Data Group is responsible for the authoritative master data for securities, accounts, and counterparties at Jefferies. The team manages end\-to\-end data ingestion from vendors and internal systems, normalization, golden record creation, and distribution to downstream consumers across the firm. We operate on a modern cloud\-native stack centered on Snowflake , AWS , and Apache Airflow , and follow engineering best practices including CI/CD, code review, and automated testing.

Key Responsibilities

• Design, build, and maintain scalable data pipelines for Security Master, Account Master, and Counterparty Master using Python and Apache Airflow .


• Develop and optimize complex data transformations, stored procedures, and views in Snowflake , ensuring high performance and data quality.


• Own the end\-to\-end lifecycle of reference data — from source ingestion and normalization through golden record creation and downstream distribution.


• Collaborate with data consumers across trading, risk, compliance, and operations to understand requirements and deliver reliable data products.


• B uild and maintain infrastructure\-as\-code and deployment pipelines using AWS services, Git , and CI/CD tooling.


• Implement data quality frameworks, lineage tracking, and monitoring to ensure the accuracy, completeness, and timeliness of reference data.


• Participate in design and code reviews, contribute to engineering standards, and mentor junior engineers.


• Work with vendors and external data providers (e.g. Bloomberg, Refinitiv) to onboard and manage data feeds.


• Contribute to platform modernization initiatives and help drive adoption of best practices across the team.


• Troubleshoot production data issues, perform root cause analysis, and implement preventative measures.

Required:

• 7\+ years of hands\-on data engineering experience


• Expert\-level Python for data engineering and automation


• Strong Snowflake experience — SQL, stored procedures, streams, tasks, and performance tuning


• Production experience with Apache Airflow — DAG design, scheduling, dependency management


• Solid AWS cloud experience — S3, Lambda, Glue, IAM, or equivalent services


• Proficient with Git, branching strategies, pull requests, and code review workflows


• Experience with CI/CD pipelines — GitHub Actions, Jenkins, or equivalent


• Strong understanding of data modelling — dimensional, relational, and hub\-spoke patterns


• Experience building and operating production\-grade data pipelines at scale


• Financial services experience is preferred but not required. Strong candidates from other industries with excellent data engineering credentials and a desire to learn financial domain concepts are encouraged to apply.

• Experience with financial reference data — Security Master, Counterparty, or Account data


• Knowledge of financial instruments — equities, fixed income, derivatives, or FX


• Familiarity with data vendors such as Bloomberg , Refinitiv , or FactSet


• Experience with data governance , lineage tools, or metadata management


• Familiarity with dbt or similar transformation frameworks


• Exposure to Kafka or event\-driven data architectures


• Experience in a regulated financial services environment

• Communication: Ability to clearly articulate technical concepts to non\-technical stakeholders including business analysts, traders, and senior management.


• Collaboration: Strong team player who works effectively across engineering, business, and operations teams in a fast\-paced environment.


• Problem Solving: Analytical mindset with a track record of diagnosing complex data quality and pipeline issues in production environments.


• Ownership: Takes end\-to\-end accountability for data products — from design through delivery, monitoring, and continuous improvement.


• Adaptability: Comfortable managing multiple priorities and adapting to changing business requirements in a dynamic financial services environment.

• Opportunity to work on high\-visibility, firm\-critical data infrastructure used across global trading and operations.


• Collaborative, engineering\-led culture with strong emphasis on code quality, testing, and continuous improvement.


• Access to modern cloud tooling and the opportunity to influence platform architecture decisions.


• Exposure to a wide range of financial products and business domains across a leading global investment bank.

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