Data Engineer

Your opportunity

At Schwab, you’re empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us “challenge the status quo” and transform the finance industry together.

We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified location(s).

Schwab Asset Management (SAM) is a leading asset manager supporting mutual funds, ETFs, and managed account products governed under stringent regulatory and compliance requirements. SAM operates in a multi‑cloud, multi‑custodian, multi‑vendor ecosystem, relying on a diverse set of external platforms such as Vestmark, Aladdin, Eagle, and others to serve its investment, operational, and regulatory functions.

This role sits directly within SAM Data team, the team responsible for designing, building, operating, and enhancing SAM Data products , platform capabilities underpinning SAM Data platform.

The SAMDA Data Engineering 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 Data Engineer, 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.

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

• Design 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.

Technical Leadership & Collaboration

• Partner closely with Investment, Portfolio Management, Risk, Business Intelligence and Operations teams.
• Provide mentorship and technical guidance to junior engineers.
• Clearly communicate investment data concepts to technical and non‑technical stakeholders.

What you have

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.

In addition to the salary range, this role is also eligible for bonus or incentive opportunities.