Data Engineer

About the role
As a Data Engineer, you will be responsible for designing, building, and maintaining efficient, scalable data pipelines and infrastructure. You will work closely with data scientists, analysts, and other stakeholders to ensure that the organization’s data systems are robust, reliable, and meet the needs of various business units. Your expertise will help in integrating various data sources, implementing data models, and ensuring data quality, security, and performance.

Key Responsibilities:

Data Pipeline Development:

• Design, develop, and maintain scalable ETL/ELT data pipelines to process large volumes of structured and unstructured data.
• Integrate data from multiple sources (databases, APIs, cloud storage, etc.) into a unified data warehouse or data lake.

Data Infrastructure Management:

• Collaborate with the DevOps team to manage cloud infrastructure (AWS, Azure, GCP) for data storage and processing.
• Optimize data storage solutions, including relational databases, NoSQL databases, and data lakes.

Data Modeling:

• Design and implement efficient data models (Star Schema, Snowflake Schema, etc.) to support analytics and reporting requirements.
• Ensure data models are scalable and aligned with business objectives.

Data Quality and Governance:

• Implement data validation, monitoring, and error-handling processes to maintain high data quality.
• Collaborate with Data Governance teams to ensure compliance with data privacy regulations (e.g., GDPR, CCPA).

Collaboration and Communication:

• Work closely with data analysts, data scientists, product managers, and business stakeholders to understand data requirements.
• Provide support in troubleshooting data-related issues and ensure timely resolution.

Performance Optimization:

• Monitor and optimize the performance of data pipelines and queries for efficiency.
• Use indexing, partitioning, and other techniques to enhance database performance.

Documentation and Best Practices:

• Create and maintain comprehensive documentation of data pipelines, data architecture, and processes.
• Follow best practices for data engineering, including code versioning, testing, and automation.

​Technical Skills

• Proficiency in SQL and experience with relational databases (e.g., PostgreSQL, MySQL, SQL Server).
• Experience with ETL tools (e.g., Apache Airflow, AWS Glue, Informatica) and data warehousing (e.g., Snowflake, Redshift, BigQuery).
• Proficiency in programming languages such as Python, Java, or Scala.
• Knowledge of big data technologies (e.g., Apache Spark, Hadoop).
• Experience with cloud platforms (AWS, Azure, GCP) and related services (e.g., S3, Azure Data Lake, Google Cloud Storage).
• Familiarity with NoSQL databases (e.g., MongoDB, Cassandra).
• Experience with data modeling, schema design, and data pipeline orchestration.

Preferred:

• Experience with containerization and orchestration (Docker, Kubernetes).
• Familiarity with data governance and privacy regulations.
• Knowledge of CI/CD practices and version control systems (e.g., Git).
• Experience with monitoring and logging tools (e.g., Datadog, Splunk).

Soft Skills:

• Strong problem-solving and analytical skills.
• Excellent communication skills to collaborate with cross-functional teams.
• Attention to detail and a commitment to data quality.
• Ability to work in a fast-paced, agile environment.

Tools and Technologies:

Examples not all are required

• Data Integration: Apache Airflow, AWS Glue, Talend, Fivetran
• Data Warehousing: Snowflake, AWS Redshift, Google BigQuery
• Programming Languages: Python, SQL, Scala, Java
• Big Data Frameworks: Apache Spark, Hadoop
• Cloud Platforms: AWS, Azure, Google Cloud Platform
• Database Technologies: PostgreSQL, MySQL, MongoDB, Cassandra
• Version Control: Git, GitHub, GitLab
• Monitoring: Datadog, Grafana, Splunk

Hybrid Work

Morningstar’s hybrid work environment gives you the opportunity to work remotely and collaborate in-person each week. We’ve found that we’re at our best when we’re purposely together on a regular basis, at least three days 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.

If you receive and accept an offer from us, we require that personal and any related investments be disclosed confidentiality to our Compliance team (days vary by region). These investments will be reviewed to ensure they meet Code of Ethics requirements. If any conflicts of interest are identified, then you will be required to liquidate those holdings immediately. In addition, dependent on your department and location of work certain employee accounts must be held with an approved broker (for example all, U.S. employee accounts). If this applies and your account(s) are not with an approved broker, you will be required to move your holdings to an approved broker.

Morningstar’s hybrid work environment gives you the opportunity to work remotely and collaborate in-person each week. While some positions are available as fully remote, we’ve found that we’re at our best when we’re purposely together on a regular basis, typically three days 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.

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