Top Fintech Data Engineering Jobs

The Data Engineer will design and maintain ETL pipelines, work with AWS services, manage data warehouses, ensure data compliance, and collaborate on scalable solutions.

Lead and manage enterprise-scale data engineering teams and platforms (Databricks, cloud warehouses). Define roadmap, data architecture, DataOps/CI-CD standards, and mentor engineers. Deliver scalable ETL/ELT pipelines, integrations, and data products on cloud and on-prem systems to meet business objectives.

As a Data Engineer, you will build and maintain scalable data pipelines, implement data engineering frameworks, and support AI initiatives. You will also design processes for data cleansing, transformation, and orchestration while adhering to engineering standards.

This role involves designing and maintaining data pipelines, creating dashboards, and collaborating with stakeholders to drive analytics solutions for external data products.

The Techno-Functional Workday Architect designs end-to-end solutions for Workday HCM and integrations, managing technical delivery, stakeholder engagement, and project governance.

The Cloud Data Engineer will design, implement, and manage data solutions on Microsoft Azure, create and maintain data pipelines, and ensure data security for decision-making processes.

As a Senior Analytics Engineer, you will develop analytics products, design data models, and improve frameworks for customer experience measurement and operational efficiency.

The Data Engineering Lead will design and manage the data infrastructure, ensuring reliable, scalable data products for financial decision-making. Responsibilities include evaluating existing systems, implementing a modern data stack, ensuring data quality, and mentoring new hires.

Design, build, and own robust batch and real-time data pipelines and golden datasets using SQL and Python. Ensure data quality, performance, schema design, and SLAs while partnering across engineering, product, and business teams. Advocate tooling and best practices, document datasets, and deliver reliable analytics infrastructure at petabyte scale.

Lead cross-team projects to build scalable data models and pipelines, using AI for data pipeline development and ensuring data quality.