Senior Data Engineer Consultant

Familiarity with modern data stack tools - dbt, Airflow/Dagster, Airbyte/dlt, Snowflake/Databricks - and the ability to evaluate them in context.

Background in financial services, investment management, or due diligence workflows.

Experience with AI/ML data infrastructure - vector databases, RAG pipelines, feature stores, embedding workflows.

Track record of producing technical documentation and training materials that teams actually use after the consultant leaves.

What this role is not

This is not a staff augmentation role - the primary deliverables are knowledge transfer, architectural guidance, and strategic documents, not production code. You do not need to have used every tool mentioned - we value depth in core concepts plus the ability to quickly evaluate new tools. You do not need a research background in AI/ML, just practical understanding of how AI is changing data engineering. Financial services experience is not required but meaningfully reduces ramp-up time.

Engagement structure

Phase 1: Education, current-state assessment, landscape mapping. Deliverables: training sessions, assessment document, gap analysis, prioritized roadmap.

Phase 2: Architecture design (PostgreSQL migration plan, canonical data layer, residency architecture, governance framework), use-case specifications, tool recommendations, and reference materials.

Ongoing advisory (based on output): Periodic architecture reviews, design consultations, and progress check-ins during execution.

Time commitment - 4-5 hrs during initial phase with availability during US working hours (6-11pm IST) required. Time commitments can be reviewed later and be extended if needed. Why Join Us?

Global Influence: Design for a platform used by the world's largest financial institutions in 150+ countries.

Startup Energy: A flat hierarchy where your work is shipped quickly and has a direct impact.

Remote Flexibility: Work from anywhere in India while collaborating with a global team.