Principal Data Engineer designing and implementing data solutions that ensure trust and transparency in supply chains. Collaborating with global teams and mentoring fellow engineers in data practices.
About the role
- Lead Data Engineer enabling Shure’s global analytics and data strategy through scalable analytics solutions. Collaborating with stakeholders to design, build, and support robust data pipelines.
Responsibilities
- Lead and perform data engineering development for connecting source systems to Shure’s data lake, ensuring data quality and governance standards.
- Assist internal business partners in implementing Generative AI-driven analytics solutions, providing guidance on use cases, integration strategies, and best practices to enhance data insights and decision-making.
- Collaborate with business partners to define analytic use cases, develop business cases, and ensure benefits realization through measurable outcomes and value delivery.
- Design and implement ETL processes to extract, transform, and load data from multiple sources into Shure’s data lake and data warehouse.
- Develop and maintain scalable, flexible data models and analytic solutions aligned with business requirements.
- Drive technical solution design and provide approvals for documentation, proposals, and final designs.
- Establish and maintain data lineage and cataloging to enable discoverability and support governance initiatives.
- Oversee code reviews, testing, and validation to ensure high-quality deliverables and adherence to standards.
- Provide technical leadership and mentorship to data engineers, serving as the first escalation point for complex issues.
- Collaborate with cross-functional teams and external contacts, offering thought leadership on emerging technologies and trends.
- Manage medium to large-scope projects, including planning, resource allocation, and execution of technical phases while supporting business users through Service Desk requests.
- Lead continuous improvement initiatives, including automation, monitoring, and reliability enhancements for the data platform.
Requirements
- Minimum of 10 years of related experience with a Bachelor’s degree; or 8 years with a Master’s; or 5 years with a PhD; or equivalent experience in enterprise data warehouse/data lake platforms and big data technologies.
- Strong professional judgment, problem-solving, and communication skills to analyze complex data challenges and deliver well-structured solutions.
- Expertise across end-to-end data processes, including application integration touchpoints and alignment of local/global requirements.
- Hands-on experience with AWS Data Engineering stack (S3, Glue, Lambda, EMR/Spark, Step Functions, Athena, Lake Formation, IAM, Redshift).
- Advanced proficiency in Snowflake (data modeling, performance tuning, Snowpipe, Streams/Tasks, RBAC, cost governance, query optimization).
- Skilled in designing and implementing CI/CD pipelines for data engineering workflows using GitLab, GitHub Actions, Azure DevOps, Terraform/CDK, and dbt.
- Experience integrating ERP (SAP, Oracle) and CRM (Salesforce) data into cloud platforms.
- Knowledge of structured/unstructured data and integration protocols (OData, ODP, REST APIs, JDBC/ODBC, file-based, custom interfaces).
- Strong understanding of modern data architecture patterns (Medallion, Lambda, Kappa, Data Mesh), dimensional and Data Vault modeling, and data quality methods.
- Expertise in SQL, shell scripting, Python or PySpark for data engineering and automation.
- Familiarity with Machine Learning concepts, Generative AI, and enabling ML pipelines and feature stores.
- Experience with real-time streaming technologies (Apache Kafka, AWS Kinesis, MSK) for high-throughput, low-latency pipelines.
- Knowledge of data visualization tools (Tableau, Power BI) and project management methodologies (SAFe Agile, Waterfall).
- Ability to rapidly learn new technologies and convert business requirements into technical designs.
Benefits
- comprehensive healthcare
- mental health and retirement savings plans
- generous paid time off programs
- employee discounts
- professional development opportunities
- work-life balance initiatives
- employee recognition programs
- volunteering/community involvement opportunities
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