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Confluence Technologies

Senior Data Engineer

Data & ML

Employment

Not stated

Level

Senior

Category

Data & ML
Not doable from Austria

Country assessment

Not doable from Austria because the employer's own board marks it as not remote.

Our assessment is guidance. Confirm arrangements with the employer.

Skills mentioned in this posting

PythonAWSGCPAzure

Job description

Location - Pittsburgh, Boston, Toronto or Montreal Why Confluence? At Confluence, we’ve always been driven by a commitment to innovation, precision, and partnership in the investment data space. Our global footprint now spans multiple countries, giving our employees the opportunity to get exposure to other countries and cultures. And it stands to reason that none of this would have been possible without the hundreds of hard-working employees who work at Confluence.

We’re looking for a Senior Data Engineer to help design and deliver scalable data solutions that power decision-making across the organization. In this role, you’ll take ownership of key components of the data platform, working hands-on with modern tools and technologies to build reliable, high-quality data pipelines. You’ll collaborate with teams across the business, contribute to architectural decisions, and support the growth of other engineers through mentorship and knowledge sharing.

This is a developer-focused role, with a strong emphasis on solving complex data challenges through clean, maintainable code. Why Join Us? Work on meaningful data challenges that have real business impact Collaborate with a supportive and knowledgeable team Opportunity to influence architecture and engineering standards Grow your leadership skills through mentoring and project ownership A pplicants must be within a commutable distance (within 2 hours or 80 miles) of one of the listed offices.

Essential Experience Solid Python experience in a data engineering environment Advanced SQL skills and hands-on experience with Snowflake Experience designing and running production-grade ETL pipelines at scale Exposure to cloud platforms such as AWS, Azure, or GCP Ability to take ownership of work end-to-end, from design through to deployment and monitoring Nice to Have Experience working with financial or regulated data environments Familiarity with orchestration tools such as Airflow or AWS Step Functions Exposure to streaming or near real-time data pipelines