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Jefferies

Senior Data Engineer - Reference Data (Assistant Vice President)

London
Posted 1 day ago
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Job Description

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.
  • 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.

Reasons to use Rodeo

I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?

Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.

Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.

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Graduate Consultant — 2026 Scheme

PwC·London, UK
£35,000/yr

Why you're a good match

Strong

Your economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.

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Why you're a good match

You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.

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Experience fit

Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.

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Required

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.

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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.

About Us

Jefferies is a leading global, full-service investment banking and capital markets firm that provides advisory, sales and trading, research, and wealth and asset management services. With more than 40 offices around the world, we offer insights and expertise to investors, companies, and governments.

At Jefferies, we believe that diversity fosters creativity, innovation and thought leadership through the infusion of new ideas and perspectives. We have made a commitment to building a culture that provides opportunities for all employees regardless of our differences and supports a workforce that is reflective of the communities where we work and live. As a result, we are able to pool our collective insights and intelligence to provide fresh and innovative thinking for our clients.

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.

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Skills

Python
Snowflake
Apache Airflow
AWS
SQL
Git
CI/CD
Data Modelling
Data Pipeline Engineering
Reference Data Management
Infrastructure as Code
Data Quality Frameworks

Location

London, England, United Kingdom

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