Data Observability Engineer
About The Position
The Data Observability Engineer supports the design, development, and maintenance of systems and infrastructure used to collect, store, process, and analyze data. The role works closely with data engineers, data scientists, analysts, QA engineers, business process and functional SMEs, clients, and other stakeholders to make data accessible, reliable, and secure. Work includes data ingestion, transformation, integration, pipeline development, data governance, and software development and deployment practices that help shorten delivery time for data products, pipelines, and data warehousing. This position provides technical guidance and mentorship to data engineering colleagues and serves as a technical contact for clients and stakeholders. It develops and maintains data pipelines, ETL processes, workflows, data models, and schemas; improves storage and processing performance and scalability; and supports tool selection and engineering standards. It also contributes to data governance, security, data quality, and integrity through validation and cleansing practices. The role is based at the Sherwin-Williams Cleveland Headquarters office and is not hybrid or remote. Applicants must be at least 18 years old and legally authorized to work in the country of employment without current or future employment visa sponsorship.
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, or Information Technology, or at least 3 years of data engineering experience in lieu of a degree.
- At least 2 years of data engineering experience.
- At least 2 years of SQL experience, including PostgreSQL, T-SQL, PL/SQL, or SNOWSQL.
- At least 1 year of experience building applications, system integrations, and web services.
- At least 1 year of server-side scripting and programming in a Linux environment, primarily bash shell.
- Experience with common database technologies such as Oracle and Snowflake, including performance tuning and optimization.
- Data manipulation skills and experience with on-premises and cloud-based data warehouse ETL tools and processes.
- Experience with version control tools.
- Data modeling experience.
- Must be at least 18 years of age.
- Must be legally authorized to work in the country of employment without needing employment visa sponsorship now or in the future.
Nice To Haves
- At least 2 years of hands-on experience building monitoring, alerting, or observability for data or application pipelines; experience with Splunk, Datadog, Dynatrace, Grafana, Prometheus, or ELK qualifies.
- Experience with data quality or data observability frameworks, such as freshness, volume, and schema-drift checks.
- Experience with log aggregation and parsing, metrics collection, and dashboard design.
Responsibilities
- Collaborate with data engineers by seeking and providing technical guidance, mentorship, and support.
- Serve as a technical point of contact for clients and stakeholders, providing updates and addressing data-related technical inquiries or concerns.
- Design, develop, and maintain efficient, robust data pipelines, ETL processes, and data workflows.
- Develop and maintain data models and schema designs to support data storage and retrieval.
- Optimize data storage, processing, and retrieval mechanisms for performance and scalability.
- Assist with technical leadership and guidance in selecting and implementing data engineering tools and technologies.
- Identify and implement best practices and standards for data engineering processes and tools.
- Stay current with emerging data engineering technologies and trends and evaluate their potential impact on data infrastructure.
- Collaborate with cross-functional teams to integrate data sources and enable seamless data flow across systems.
- Work with data scientists, analysts, and stakeholders to understand data requirements and develop scalable data solutions.
- Support implementation and maintenance of data governance and security measures to protect sensitive information.
- Help ensure data quality and integrity through effective data validation and cleansing techniques.
Benefits
- A wide range of benefits related to retirement, health care, well-being, and commuting.
- Rewards, benefits, and flexibility intended to support health and well-being.
- Opportunities to learn, develop new skills, and grow.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
