Innovation Hub Overview
Jefferies is creating a Technology Innovation Hub in Pune, a greenfield opportunity to build the systems that power global markets. As our first India technology center, this hub brings together hands on builders who engineer the platforms behind Jefferies’ growth across capital markets, investment banking, and institutional securities. We’re scaling toward an elite team of 500 engineers while maintaining the agility, ownership, and meritocratic spirit that defines Jefferies. From cloud and data to AI, risk, and core business technologies, teams in Pune will lead high impact work with a global mandate.
IT Corporate Technology
Corporate Technology powers Jefferies’ enterprise operations, enabling front-, middle-, and back-office teams to run efficiently, securely, and in compliance at scale. The group delivers and supports integrated platforms across HR, Finance, Compliance & Legal, Treasury, Client Onboarding, Operations, Centralized Data Services, Regulatory Reporting, Loan Operations, and FX Trading, while also leading firmwide digitalization initiatives focused on automation and process efficiency.
The team uses modern technology stack—including AWS and Azure, Snowflake, Qlik Sense, and best-in-class vendor solutions—to build next-generation capabilities in AI/ML, enterprise analytics, and process automation (RPA and low-code/no-code). Roles in Corporate Technology offer hands-on work with cloud, data, and automation platforms, close collaboration with engineers, product managers, and business stakeholders, and end-to-end exposure to how a global financial services firm operates
Job Summary
We are seeking a skilled and detail-oriented Data Engineer to join our cross-functional Agile team. In this role, you will be responsible for designing, building, and maintaining modular, reliable data transformation pipelines using dbt (Data Build Tool) in a Snowflake environment. You will collaborate closely with backend and frontend engineers, product managers, and analysts to create analytics-ready data models that power application features, reporting, and strategic insights. This is an exciting opportunity for someone who values clean data design, modern tooling, and working at the intersection of engineering and business.
Key Responsibilities
- Design, build, and maintain scalable, modular dbt models and transformation pipelines using DBT Core.
- Understand DBT Architecture thoroughly and experience in writing Python operators in DBT flow. Strong experience in writing Jinja code, macros, seeds etc.
- Design and develop data warehouses using Snowflake.
- Build and maintain ETL/ELT pipelines to load data from multiple sources.
- Optimize Snowflake performance (Clustering, Warehouses, Query tuning).
- Implement data security (roles, access control, masking)
- Knowledge of data modeling techniques like data vault and dimensional modeling (Kimball/Inmon).
- Automate workflows using data orchestration tools ( Apache Airflow or Snowflake Tasks )
- Collaborate with full-stack developers and UI/UX engineers to support application features that rely on transformed datasets.
- Work closely with analysts and stakeholders to gather data requirements and translate them into reliable data models.
- Enforce data quality through rigorous testing, documentation, and version control in dbt.
- Participate in Agile ceremonies (e.g., stand-ups, sprint planning) and manage tasks using Jira .
- Integrate dbt into CI/CD pipelines and support automated deployment practices.
- Monitor data performance and pipeline reliability, and proactively resolve issues.
Mandatory Qualifications & Skills
- 6-11 years of experience in data engineering or analytics engineering , with a focus on SQL-based data transformation.
- Hands-on production experience using dbt core or dbt cloud as a primary development tool.
- Strong command of SQL and solid understanding of data modeling best practices (e.g., star/snowflake schema).
- Proven experience with Snowflake as a cloud data warehouse.
- Python skills for data pipeline integration or ingestion.
- Familiarity with Git-based version control workflows.
- Strong communication and collaboration skills, with the ability to work across engineering and business teams.
- Experience working in Agile/Scrum environments and managing work using Jira .
Nice-to-Have Skills
- Exposure to CI/CD pipelines and integrating dbt into automated workflows.
- Experience with cloud platforms such as AWS .
- Understanding of how data is consumed in downstream analytics tools (e.g., Sigma , Qlik , Power BI ).
- Familiarity with Docker and container-based development.
Education & Experience
- Bachelor’s degree in computer science, Data Engineering, or related field.
- A track record of building and maintaining scalable dbt projects in a production setting with Snowflake and Airflow.
- Experience working in cross-functional teams involving developers, analysts, and product managers.
- A strong sense of ownership, documentation habits, and attention to data quality and performance.
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