We are currently seeking Data Engineers to join a large-scale greenfield data modernization initiative for a Financial Services client.

Summary

We are seeking three Data Engineers to join a large-scale greenfield data modernization initiative for a Financial Services client. The team is building an enterprise data platform from the ground up, integrating more than 30 operational source systems into a centralized Lakehouse architecture.

The primary focus of this role will be developing data ingestion, transformation, and modeling solutions that support the creation of a modern data lakehouse and semantic data layer. The client has dedicated Business Analysts and architects responsible for requirements gathering, data design, and source-to-target mapping. Data Engineers will be responsible for implementing and validating those designs through scalable, production-quality data pipelines.

This is an excellent opportunity to work on a long-term strategic initiative that will evolve over the next 12 months, culminating in a curated Gold Layer that supports enterprise analytics, KPI reporting, and future business intelligence initiatives.

Responsibilities:

• Develop and maintain data pipelines that integrate data from 30+ operational source systems.
• Build and support a modern Lakehouse architecture using Microsoft Fabric and Azure technologies.
• Implement source-to-target mappings for business entities, dimensions, and fact structures.
• Develop ETL/ELT processes using Azure Data Factory (ADF).
• Create scalable and maintainable data transformation processes using Python and SQL.
• Support the development of semantic models and KPI-driven data structures.
• Validate data quality and perform unit testing on all developed solutions.
• Collaborate with architects, Business Analysts, and stakeholders to ensure successful delivery.
• Troubleshoot and optimize data processing performance and reliability.
• Follow established development standards and best practices.

Required Qualifications:

• 3–5 years of professional Data Engineering experience.
• Strong experience with Azure-based data platforms.
• Hands-on experience developing ETL/ELT pipelines using Azure Data Factory (ADF).
• Strong Python programming skills.
• Advanced SQL development skills.
• Experience with data warehousing, dimensional modeling, and Lakehouse concepts.
• Experience integrating data from multiple enterprise source systems.
• Strong understanding of data quality validation and unit testing practices.
• Ability to work independently in a distributed team environment.

Preferred Qualifications:

• Experience with Microsoft Fabric is highly preferred.
• Candidates with strong Databricks experience will also be considered, particularly those with experience building modern Lakehouse architectures, scalable data pipelines, and enterprise data platforms on Azure.
• Experience building semantic models and business KPI frameworks.
• Experience supporting enterprise analytics initiatives.
• Power BI experience is a plus but is not required during the initial phase of the project.
• Financial Services industry experience is preferred.

Additional responsibilities include:

• Conducting code reviews and ensuring adherence to development standards.
• Providing technical leadership and mentoring to other Data Engineers.
• Reviewing unit testing and data validation processes.
• Ensuring overall delivery quality and technical consistency.
• Collaborating with architects and project leadership on technical decisions and implementation approaches.

Interview Process

Interview 1 – Technical Assessment (60 Minutes)

Focus Areas:

• Azure Data Engineering
• Azure Data Factory
• Python
• SQL
• Data Modeling
• Fabric and/or Databricks
• Data Lakehouse Architecture

Interview 2 – Culture & Team Fit

• Communication skills
• Collaboration style
• Problem-solving approach
• Client engagement and ownership mindset