Sr Specialist, Data Platform Engineering

Your opportunity

Schwab Description :

At Charles Schwab, our purpose is simple: we champion client’s goals with passion and integrity. Guided by honesty, mutual respect and a commitment to doing what’s right, we bring innovation, education, and service together to help shape financial futures. Our people are the foundation of our success – they approach their work with curiosity and collaboration, coming together to create solutions that make a meaningful impact for clients and communities. As we expand into India, we are bringing this same culture of inclusion, learning, and opportunity to new talent. Joining us means becoming part of a global team where your work matters and your future can take shape.

Schwab India:

ur Hyderabad location is central to Schwab’s growth, bringing together talented people and technology to drive innovation, scale and efficiency. Here, you will work alongside teams who create solutions that support millions of clients every day. The work you do is more than daily operations – it’s a chance to experiment, learn, and build within a values-driven, supportive environment. This is a unique opportunity to be part of our early growth phase and shape something new, backed by the stability and strength of a Fortune 500 company. Your impact begins on day one, and your contributions will help define our future in the region​.​

What you have

Role Description:

The SAM Data Technology team builds and enhances cloud native data pipelines and data platform capabilities that support Schwab Asset Management’s analytical, operational, and regulatory data needs. As a Sr. Specialist (Level 56), you will take on expanded ownership of data pipeline design, cloud data engineering patterns, and the development of scalable data solutions across Snowflake and Google Cloud Platform (GCP).
Engineers at this level operate with greater independence, take lead roles in technical problem solving, and contribute to shaping best practices for data engineering within SAMDA.

Key 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.
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Required Qualifications :

Bachelor’s degree in Computer Science, Information Technology, or equivalent experience.
•3–5 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 dashboards/UI (Streamlit, Dash, Panel).
•Experience with distributed processing frameworks (Spark, Beam, or Flink).
•Proficiency with CI/CD, Docker, and IaC (Terraform or GCP Deployment Manager).

Preferred Qualifications:

Experience with investment management data (portfolios, performance, risk, pricing).
•Familiarity with custodians, portfolio accounting, or market data vendors.
•Knowledge of data governance, entitlements, and controls in regulated investment environments.
•Ability to lead cross functional investment data initiatives.
•Strong documentation and communication skills