Cloud Data Engineer

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.

Schwab Technology Services enables the future of how clients manage their money by providing innovative and reliable technology products and services as part of our ongoing commitment to democratize access to investing and

financial planning.

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 the shared platform capabilities underpinning SAM Data platform. As a junior platform engineer, you will help expand

and operate the cloud infrastructure, CI/CD tooling, orchestration layers, and Snowflake resources that support all SAMDA tenants.

The SAM Data team supports Schwab Asset Management by building and enhancing cloud‑native data solutions that enable high‑quality, scalable, and reliable access to SAM datasets. As a Data Engineer (Level 55), you will contribute

directly to the development and optimization of data pipelines, data models, and cloud‑native processing patterns that power analytics, regulatory reporting, and operational workflows.

This role is well‑suited for an early‑career engineer who wants to deepen their skills in data engineering on Snowflake and Google Cloud Platform (GCP) while working in a collaborative environment.

Data Pipeline Engineering

Build and enhance cloud‑native data pipelines using ETL/ELT and data‑integration patterns on GCP and Snowflake.

Develop reliable and repeatable data workflows using services such as GCS, Dataproc, Cloud Dataflow, Cloud

Composer (Airflow), and Cloud Pub/Sub.

Create scalable, well‑structured data transformations leveraging Snowflake and cloud‑warehouse patterns.

Data Modeling & Storage

Apply modern data‑modeling techniques, including RDBMS, NoSQL, star schema, and Kimball‑style designs.

Work with distributed processing technologies such as Apache Spark, Apache Beam, and Apache Flink to organize,

transform, and prepare data for consumption. Cloud Data Engineering

Engineer solutions using key GCP services, including Cloud Storage, Cloud Run, Cloud Functions, Pub/Sub, Composer,

and Cloud SQL

Leverage cloud‑native tools to build scalable, observable data workflows.

DevOps & CI/CD

Utilize build and deployment tools such as Git/Bitbucket/Bamboo, Maven, Jenkins, Nexus, and containerization

Required Qualifications

Bachelor’s degree in Computer Science, Information Technology, or a related field, or equivalent practical experience.

2+ years of experience as a cloud data platform engineer in a data‑analytics ecosystem, with progression in technical responsibilities.

1 + year of Schwab technology domain experience gained as a current or recent contractor or employee

Hands‑on experience with Snowflake and GCP services such as Cloud Storage, Cloud Run, Cloud Functions,Pub/Sub, Composer, and Cloud SQL.

Proficiency with Infrastructure‑as‑Code tools such as Terraform or Google Cloud Deployment Manager.

Strong analytical and problem‑solving skills with the ability to work independently and within a team.

Effective technical communication skills and attention to detail.

technologies.

Apply continuous integration and deployment practices using Docker, GitHub, and GitHub Actions.

Data Quality & Reliability

Ensure data quality and reliability by implementing validation checks and designing robust data‑processing logic.

Work collaboratively to troubleshoot data issues and continuously improve pipeline stability.

Preferred Qualifications

Experience with secure data‑management practices, including data governance and data‑quality management for pipelines. Deep understanding of modern data‑architecture patterns for data‑pipeline and reporting environments. Experience supporting cross‑functional initiatives and strong documentation skills. Cloud Platforms: GCP (Cloud Storage, Cloud Run, Cloud Functions, Pub/Sub, Composer, Cloud SQL) [ Data Warehouses: Snowflake, BigQuery , Postgres Processing Frameworks: Spark, Beam, Flink DevOps: Git, Bitbucket, Bamboo, Jenkins, Maven, Nexus, GitHub Actions, Docker