Who Are We? The Motley Fool is a purpose-driven financial services company on a mission to make the world smarter, happier, and richer. For 30 years, we’ve been helping people make better investment decisions through transparency, education, and a healthy dose of Foolish fun.

We’re a fast-moving, collaborative team that values high-quality work, curiosity, and initiative. We care deeply about what we do, and we’re driven by the impact our work has on real people’s financial futures. What Does This Team Do?

We are seeking a Senior Data Engineer to design, build, and take full ownership of the data infrastructure powering our investment operations. You will own the complete data lifecycle, spanning ingestion, transformation, warehousing, analytics, and machine learning, serving as the connective tissue between raw data and the decisions that depend on it. If something breaks, you fix it.

If a stakeholder needs data, you find it or build it. The Investment Committee and business partners rely on you to ensure they always have trustworthy, timely data in hand. You will architect ETL/ELT pipelines orchestrated by Apache Airflow (AWS MWAA), build and tune a Snowflake data warehouse fed by S3 data lakes, and develop the analytical models and dashboards that turn raw data into actionable intelligence.

You will also play a key role in introducing machine learning capabilities to enhance forecasting, anomaly detection, and portfolio analytics. What Will You Do in This Role? You will help lead the transformation of our data infrastructure, moving the team from manual processes and spreadsheet-based workflows into scalable, governed, automated data systems.

This role sits at the center of investment operations, analytics, reporting, and emerging AI initiatives. What Strategic Initiatives You Will Drive? The “Golden Source” Transformation: Migrating data reliance from spreadsheets and manual processes into a governed Snowflake warehouse with documented lineage, quality checks, and self-service analytics.

Automated Ingestion Pipelines: Replacing manual file drops with event-driven Airflow DAGs and AWS Lambda functions that ingest, validate, and transform data from external vendors and internal systems in near real-time. Infrastructure as Code: Defining all cloud infrastructure with Terraform or AWS CDK so that development, staging, and production environments are reproducible, version-controlled, and auditable. Data-Powered AI Initiatives: Establishing the data foundations that enable AI across the business: clean, governed, and accessible datasets that feed AI agents, natural-language interfaces, and intelligent automation.

Machine learning techniques such as anomaly detection, classification, and forecasting will augment these initiatives where appropriate. Automate how we tell our story with data: Build reusable templates and automation frameworks to close the loop between our database and the materials our team uses to win and retain business. That means pulling live data into branded one-pagers, generating narrative-driven slide decks, websites, populating email campaigns, and producing social-ready content.

Okay, but what will you actually do in this role? Data Engineering ETL — 35% Pipeline Design Orchestration: Design, build, and maintain robust ETL/ELT pipelines using Apache Airflow (MWAA). Author DAGs that handle complex dependencies across external data vendors, internal models, and downstream consumers.

Data Integration: Ingest data from diverse sources including SFTP feeds, REST APIs, flat files, and third-party financial data providers. Normalize and conform data into a consistent analytical model. Data Quality Monitoring: Build “circuit breakers” into pipelines: automated data quality checks that halt downstream processing and alert the team via CloudWatch and Slack when anomalies are detected.