Datawarehouse Architect with Capital market Domain

We are

At Synechron, we believe in the power of digital to transform businesses for the better. Our global consulting firm combines creativity and innovative technology to deliver industry-leading digital solutions. Synechron’s progressive technologies and optimization strategies span end-to-end Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. Through research and development initiatives in our FinLabs we develop solutions for modernization, from Artificial Intelligence and Blockchain to Data Science models, Digital Underwriting, mobile-first applications and more. Over the last 20+ years, our company has been honored with multiple employer awards, recognizing our commitment to our talented teams. With top clients to boast about, Synechron has a global workforce of 16,850+, and has 60 offices in 20 countries within key global markets.

Our challenge

We are looking for a highly proficient Data Warehouse Architect to lead the design and implementation of our enterprise data solutions. The ideal candidate will possess strong expertise in Azure Databricks, Python, and have a deep understanding of the financial or capital markets domain to support our data-driven initiatives and strategic insights.

Additional Information*

The base salary for this position will vary based on geography and other factors. In accordance with law, the base salary for this role if filled within New York, NY is $140k - $155k/year & benefits (see below).

The Role

Responsibilities:

• Design, develop, and optimize scalable data warehouse architectures leveraging Azure cloud services.
• Build and maintain robust data pipelines and workflows using Azure Databricks and Python.
• Collaborate with business teams to understand data requirements and translate them into effective technical solutions.
• Ensure high data quality, security, and compliance with industry standards and regulations.
• Lead efforts to migrate legacy data systems to modern cloud-based architectures.
• Develop data models, ETL/ELT processes, and performance tuning strategies.
• Provide technical leadership, mentorship, and guidance to data engineering teams.
• Monitor data pipelines for reliability and troubleshoot issues as they arise.
• Stay current with emerging technologies, tools, and best practices in big data, cloud computing, and financial data management.

Requirements:

• Data Governance & Compliance:
• Define and operationalize the Data Governance framework covering data ownership, stewardship, classification, lineage, and quality SLAs in collaboration with the CDO and Compliance teams.
• Implement automated end-to-end data lineage capabilities to support audit trails and regulatory examinations.
• Establish and monitor data quality dimensions such as completeness, accuracy, consistency, and timeliness with automated profiling and alerting for critical financial datasets.
• Drive data cataloguing initiatives, business glossary management, and metadata enrichment to improve enterprise-wide data democratization.
• Ensure adherence to financial industry governance, security, privacy, and regulatory requirements.
• Stakeholder Engagement & Leadership:
• Act as a trusted technical advisor to senior stakeholders on enterprise data strategy and platform capabilities.
• Lead and mentor a team of data engineers through architectural guidance, code reviews, and technical coaching.
• Partner with Enterprise Architecture and Infrastructure teams to ensure alignment with bank-wide technology standards.
• Produce clear technical documentation, architecture diagrams, and executive-level presentations for technical and non-technical audiences.
• 12+ Years experience as a Data Warehouse Architect or in a similar senior data engineering role.
• Hands-on experience with Azure Databricks, Python, and related Azure Data Services.
• Strong proficiency in SQL and data modeling techniques.
• Experience developing data pipelines, ETL/ELT workflows, and working with big data technologies such as Spark.
• Deep domain knowledge of