About the Role

As a Lead Software Engineer on our Data Feed Platform team (Direct – Data & Research), you will partner with product owners and engineering teams to shape the technical direction of our data engineering capability. Together, you will migrate our file-based products to a unified, cloud-native data platform — architecting highly governed data pipelines, feed generation systems, and large-scale data delivery infrastructure.

This is a senior individual contributor role reporting to the Director of Technology. You will serve as a technical thought leader for a team of engineers — owning the end-to-end data platform architecture, from ingestion and transformation through to client-facing data products. You will define best practices for data governance, data modeling, performance optimization, and data reliability across the entire product lifecycle, while mentoring engineers and fostering continuous improvement within the data engineering discipline.

If your background is in data engineering, data platform development, or building production-grade data systems at scale, this role was designed for you.

Key Responsibilities

• Lead technical direction across data feeds and the data engineering function, guiding architectural decisions across platforms.
• Collaborate with engineers on the platform consolidation strategy, migrating legacy feed products onto a unified, governed, cloud-native architecture.
• Design and implement scalable data delivery mechanisms for both file-based feeds and modern marketplace distribution platforms.
• Influence technical strategy across teams by communicating architectural vision to both technical and non-technical stakeholders.
• Core Qualifications (Must Haves)

Experience: 

• 7+ years of experience in data engineering, data platforms, or distributed systems.
• Cloud Expertise: Experience with large-scale data pipelines on a major cloud platform (AWS preferred; Azure or GCP also accepted).
• SQL & Programming: Experience in SQL (Postgres, SQL Server, etc) and Python (Python 3.x).
• Technical Leadership: Ability to influence engineering direction without direct management authority, mentor engineers, and drive alignment across teams.
• Deployment: Experience with containerization (Docker, Kubernetes).
• Cloud Storage: Hands-on experience with cloud object storage (AWS S3, Azure Blob Storage, or Google Cloud Storage).

Nice to Have (Experience & Tools)

• Data Warehousing: Strong hands-on experience with modern cloud data warehouses (e.g., Snowflake, Databricks, Redshift).
• Architecture: Knowledge of data lake and Lakehouse architecture and familiarity with open table formats like Delta Lake and Apache Iceberg.
• Domain Knowledge: Previous experience in highly regulated or financial services industries with stringent data quality and delivery SLA requirements.
• AI-Assisted Development: Experience using agentic coding tools (e.g., GitHub Copilot, Claude Code, Cursor) to accelerate development workflows.

Base Salary Compensation Range

Incentive Target Percentage

Morningstar's hybrid work environment gives you the opportunity to collaborate in-person each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days in-office each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you'll have tools and resources to engage meaningfully with your global colleagues.