Data Engineering Lead

Charles Schwab Inc.•Westlake, TX

1d•$157,000 - $205,000

About The Position

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 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. We are seeking a Lead Data Engineer to drive the design and development of the cloud-native Data Platform for Schwab Asset Management (SAM). In this role, you will design and deliver end-to-end data solutions, not just pipelines—spanning raw data ingestion, curated data layers, enterprise data hubs, and the APIs and services that power downstream applications and analytics. You will work across a modern cloud data stack built on Snowflake and Google Cloud Platform (GCP to build scalable, resilient, and reusable platform capabilities.

Requirements

Bachelor’s degree in computer science, Engineering, or related field (or equivalent practical experience).
6–8+ years of experience in cloud-native data engineering.
Strong experience working on modern cloud data stacks using GCP and/or AWS.
Deep, hands-on experience with cloud data warehouses (Snowflake preferred) and Apache Spark based data pipeline development
Strong experience in data pipeline orchestration leveraging platforms like Apache Airflow
Proven experience designing and delivering: Medallion data architectures, Semantic data layers, Analytics-ready and AI-ready datasets
Expert-level SQL and strong Python skills.
Ability to operate independently and lead technically without formal authority.

Nice To Haves

Hands-on experience modeling investment data domains and building curated Investments data products for consumption across Investments management business functions.
Designing and enforcing data quality frameworks at scale.
Implementing data governance capabilities, including metadata, lineage, and controlled access.
Defining and managing data contracts between upstream producers and downstream consumers.
Supporting analytics, BI, and AI / ML workloads.
Acting as a technical lead on complex data initiatives.

Responsibilities

Design, build, and operate cloud-native data pipelines using GCP and/or AWS.
Lead development of scalable ELT/ETL workflows supporting investment, operational, regulatory, and analytics use cases.
Serve as a Snowflake subject-matter expert, designing advanced data models, transformations, and performance-optimized workloads.
Engineer and curate data within cloud data warehouses and cloud-native data platforms, ensuring data is analytics-ready and AI-ready.
Design data hubs and domain data products that serve as authoritative sources for shared datasets, reducing duplication and ensuring consistent enterprise-wide data usage.
Optimize data solutions for performance, scalability, reliability, and cost efficiency.
Design and implement medallion data architectures (Bronze / Silver / Gold).
Build and evolve semantic data layers that provide consistent, reusable business metrics.
Design and curate AI-ready datasets to support advanced analytics, machine learning, and generative-AI use cases.
Leverage Snowflake’s AI capabilities, including Snowflake Cortex and native Snowflake AI solutions, as part of the modern data architecture to enable intelligent data access, enrichment, and downstream AI workflows.
Ensure architectural alignment between curated data, semantic layers, and AI-enabled consumption patterns.
Lead complex data-modeling efforts across investment domains, including holdings, positions, transactions, securities, portfolios, benchmarks, performance, and reference data.
Apply investment domain knowledge to ensure models accurately represent real-world investment behavior and lifecycle events.
Define, implement, and enforce data quality standards, including validation rules, completeness checks, reconciliations, and anomaly detection.
Apply data governance principles, including metadata management, lineage, access controls, and policy enforcement.
Design and implement data contracts to define schema expectations, ownership, SLAs, and change-management between data producers and consumers.
Act as a technical lead for complex data-engineering initiatives and investment-domain data products.
Drive architecture discussions, design reviews, and technical decision-making.
Mentor junior and mid-level engineers through code reviews and technical guidance.
Partner closely with platform engineering, architecture, analytics, and business stakeholders.