Python Data Engineer

Client is placing a senior data integration consultant embedded at a financial services client. The consultant works onsite as part of the client's data engineering effort, integrating the client's systems and data sources with Client's Declarative Agentic Framework (DAF) and connected products.
This is delivery work, not advisory. The consultant owns the data pipelines, warehouse structures, and integration points that the deployment depends on, and is accountable for data quality and reliability in production.
What the Consultant Will Do

• Design and build data integration pipelines connecting client source systems (reference data, pricing, transaction, custody, or similar) to client's AI's platform .
• Own data warehouse and data lake architecture for the engagement, including ingestion, transformation, and data quality rules.
• Work hands-on with ETL tooling (Informatica, Snowflake, or equivalent) and cloud data platforms (AWS or Azure).
• Write production-grade Python for data processing and integration between on-prem and cloud systems.
• Define data reconciliation and data lineage processes so the client can trust what the pipelines produce.
• Coordinate with the client's data engineering, ops, and compliance stakeholders, and with client's AI's deployment team.
• Document architecture decisions and integration patterns so the work is defensible and repeatable across future deployments.

Required Background

• 10+ years in enterprise data architecture, data engineering, or solution architecture roles.
• Direct experience in financial services, ideally capital markets, asset management, or securities operations. Candidate should recognize terms like reference data, corporate actions, and reconciliation without explanation.
• Hands-on ETL and data warehousing expertise: Informatica, Snowflake , or comparable enterprise-grade tooling .
• Production Python for data pipelines and integration work , not scripting on the side.
• Experience with REST APIs, message queues (Kafka or similar), and containerized deployments (Docker).
• Track record owning delivery end to end, from data analysis and architecture through production support, ideally with distributed or offshore team coordination.
• Comfortable being the senior technical presence in the room with client stakeholders from day one. No ramp-up period.