Data Engineer (Investment Banking)

About the Role

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.

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.

Qualifications

• Bachelor’s degree in computer science, Data Engineering, or related field.

Required Skills

• 3–5 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.
• 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.

Preferred 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.