01Key Responsibilities
Design & Build Investment Data Pipelines
- Design, develop, and optimize investment data pipelines using ETL/ELT patterns.
- Integrate data from custodians, vendors, and internal platforms into curated datasets.
- Build reliable workflows using GCS, Dataproc, Cloud Dataflow, Composer (Airflow), and Pub/Sub.
- Implement scalable transformations in Snowflake and other cloud data warehouses.
Investment Data Modeling
- Contribute to portfolio, transaction, pricing, and performance data models aligned to investment use cases.
- Kimball/star schema patterns and domain driven modeling for analytics and reporting.
- Optimize schemas for API consumption, dashboards, and quantitative analysis.
Data APIs for Investment Use Cases (Full Stack)
- Design and implement Investment Data APIs using Python
- Expose curated investment datasets (holdings, performance, risk metrics) via secure REST endpoints.
- Enable programmatic access for portfolio managers, analysts, and downstream applications.
- authentication, authorization, and data entitlement controls consistent with regulated investment data.
Python Dashboards & Investment Analytics UI
- Build Python based dashboards and UI applications (Streamlit, Dash, Panel).
- Create interactive visualizations for portfolio views, performance trends, and risk insights using Plotly, Matplotlib, and Seaborn.
- Translate complex investment datasets into intuitive, self service analytical experiences.
- Optimize dashboard performance for large portfolios and time series data.
Advanced Cloud & Application Engineering
- Build data and application services using Cloud Run, Cloud Functions, and Cloud SQL.
- distributed processing frameworks (Apache Spark, Beam, Flink) for large scale investment datasets.
- Package and deploy APIs and dashboards using Docker.
DevOps, CI/CD & Automation
- Own CI/CD pipelines for data pipelines, investment data APIs, and Python UI applications.
- Use Git/Bitbucket/Bamboo, Jenkins, GitHub Actions, Maven, Nexus for build and release automation.
Data Quality, Controls & Reliability (Investments)
- Implement data quality checks specific to investment data (reconciliation, completeness, timeliness).
- Monitor pipelines, APIs, and dashboards to ensure reliable delivery of investment insights.
- Troubleshoot complex data issues impacting portfolio, performance, or risk reporting.
Required Qualifications:
Bachelors degree in Computer Science, Information Technology, or equivalent experience.
- 35 years of experience building cloud based investment or financial data platforms.
- Hands on experience with Snowflake and GCP services (GCS, Cloud Run, Cloud Functions, Pub/Sub, Composer, Cloud SQL).
- Strong Python skills for data engineering, API development, and visualization.
- Experience building REST APIs .
