Key skills
PythonAIETLData EngineeringData WarehousingData LakeSnowflakeKafkaAWSAzureDocker
About this role
What the Consultant Will Do
- Design and build data integration pipelines connecting client source systems (reference data, pricing, transaction, custody, or similar) to Transient.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 Transient.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.