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 Skills and Experience
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.
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.
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