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.

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.
• Build 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 Skills and Experience

• 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.

Nice to have

• 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.

Core Competencies

• 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.

What we offer

• 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.

Jefferies is an equal employment opportunity employer, and takes affirmative action to ensure that all qualified applicants will receive consideration for employment without regard to race, creed, color, national origin, ancestry, religion, gender, pregnancy, age, physical or mental disability, marital status, sexual orientation, gender identity or expression, veteran or military status, genetic information, reproductive health decisions, or any other factor protected by applicable law. We are committed to hiring the most qualified applicants and complying with all federal, state, and local equal employment opportunity laws. As part of this commitment, Jefferies will extend reasonable accommodations to individuals with disabilities, as required by applicable law.