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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringETL processesdata modelingAWS Data Engineering stackSnowflakeCI/CD pipelinesSQLPythonApache Kafkadata visualization

Soft Skills

problem-solvingcommunicationtechnical leadershipmentorshipcollaborationjudgmentanalytical skillsproject managementcontinuous improvementthought leadership