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.

Key Responsibilities:

Cloud-Native Data Engineering & Data Warehousing

· 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.

Modern Data Architecture

· 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.

Data Modeling, Quality & Governance (Investment Domain Focus)

· 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.

Technical Leadership (IC Role)

· 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.

Required Qualifications:

• 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.

Preferred Qualifications:

• 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.