Senior Data Engineer

The Motley Fool is a purpose-driven financial services company focused on making the world smarter, happier, and richer. They are seeking a Senior Data Engineer to design and manage the data infrastructure that powers investment operations, ensuring reliable and timely data for stakeholders.

Responsibilities:

• 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
• 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
• 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
• Implement AWS Lambda functions for lightweight, event-driven tasks such as triggering ingestion when files land in S3 or validating data payloads before loading
• Maintain and document the data catalog so institutional knowledge lives in the system, not in your head
• Serve as the subject-matter expert for Snowflake. Design schemas, manage data loading via Stages and Snowpipe, and implement role-based access controls
• Write advanced analytical SQL: window functions, CTEs, recursive queries, pivots; to support investment reporting, performance attribution, and ad-hoc analysis
• Profile and optimize slow-running queries. Leverage clustering keys, micro-partition pruning, materialized views, and result caching to minimize compute cost and maximize performance
• Define and deploy cloud resources using Terraform or AWS CDK. Treat infrastructure as software with version control, peer review, and automated testing
• Help design and maintain CI/CD workflows with GitHub Actions for automated testing, linting, and deployment of data pipelines, infrastructure, and application code
• Partner with investment and business teams to translate questions into data models, dashboards, and reports that drive strategic decisions using Tableau
• Design and build automated pipelines that pull data from source systems and render it into production-ready marketing outputs: one-pagers, pitch decks, email campaigns, and social content
• Be a resource for software engineers to build an AI layer on top of existing data infrastructure, enabling LLMs to securely query fund performance data via APIs and answer natural-language questions for internal stakeholders

Requirements:

• 5+ years of professional Python development
• Comfortable with object-oriented design, data manipulation libraries (pandas, NumPy)
• Familiarity with financial research data vendors and feed/API products such as CapIQ Xpressfeed, FactSet, Bloomberg, Thomson Reuters/Refinitiv/LSEG, Russell or MSCI
• Familiarity with financial business data and feed/API products from Broadridge, Morningstar and custodian banks and fund administrators
• Proven experience designing and operating ETL/ELT pipelines
• Deep expertise in Snowflake architecture (clustering keys, micro-partitions, Snowpipe, Stages)
• Able to write complex analytical SQL, window functions, CTEs, recursive queries, and optimize them for cost and performance
• Hands-on experience with AWS CDK or Terraform
• Exposure to LLM integration patterns: markdown files and prompt engineering
• Working knowledge of the AWS ecosystem: Lambda, ECS Fargate, Step Functions, S3, EventBridge, CloudWatch, and RDS
• Experience with data visualization tools (Tableau, Streamlit, or similar) for self-service analytics
• Background in data governance, data cataloging, or data lineage tooling
• Demonstrated experience maintaining CI/CD pipelines for automated testing and deployment of data engineering and application code using Github actions and Terraform
• Experience profiling and optimizing queries across both OLAP (Snowflake) and OLTP (PostgreSQL/Aurora) systems
• Familiarity with EXPLAIN plans, indexing strategies, and database-level performance tuning
• Practical experience building, evaluating, and deploying ML models
• Familiarity with common frameworks (scikit-learn, XGBoost) and an understanding of when and how to apply ML to business problems