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.
Apply 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.
Apply 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.
Apply 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 using FastAPI, Flask, or similar frameworks.
Experience building Python .
