Senior Data Engineer

Amphenol and its Affiliated Companies•Mesa, AZ

1d•$100,000 - $130,000

About The Position

Amphenol Industrial Operations is seeking a highly skilled and motivated Senior Data Engineer to lead the design, development, and optimization of enterprise data solutions that support manufacturing operations, business intelligence, and strategic decision-making across our global organization. This role is responsible for building and maintaining scalable, secure, and high-performance data pipelines and infrastructure that transform data from ERP systems, manufacturing applications, cloud platforms, remote databases, and other business systems into trusted and actionable information.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Science, or a related discipline.
6+ years of professional data engineering experience.
Strong experience designing, developing, and supporting production data pipelines.
Strong understanding of data engineering principles
Strong understanding of data modeling
Strong understanding of ETL/ELT development
Strong understanding of data quality management
Strong understanding of enterprise reporting and analytics
Experience working with large datasets and complex data structures.
Strong analytical and problem-solving skills.
Advanced experience with Microsoft SQL Server, including T-SQL, stored procedures, views, functions, indexing, query optimization, execution plans, and performance troubleshooting.
Demonstrated experience designing, developing, and supporting ETL/ELT pipelines across multiple databases, business systems, and geographic regions.
Experience developing reports, dashboards, and business intelligence solutions using tools such as Power BI, SQL Server Reporting Services, or equivalent reporting platforms.
Knowledge of ETL/ELT development concepts, including data extraction, transformation, incremental loading, scheduling, dependency management, and error recovery.
Strong knowledge of data engineering principles, architectures, design patterns, and industry best practices.
Strong understanding of relational data modeling, database normalization, dimensional modeling, data warehousing, and metadata management.
Knowledge of ETL/ELT development concepts, including data extraction, transformation, incremental loading, scheduling, dependency management, and error recovery.
Strong understanding of data quality management, including validation, reconciliation, completeness checks, duplicate prevention, auditing, and exception handling.
Ability to integrate data from ERP systems, SharePoint, APIs, flat files, external applications, cloud platforms, and remote databases.

Responsibilities

Design and implement robust, scalable ETL/ELT pipelines across multiple SQL Server environments and cloud platforms.
Collaborate with developers, and business stakeholders to understand data requirements and translate them into technical solutions.
Develops and maintains comprehensive documentation of data processes, reports, applications, and procedures to ensure consistency, knowledge sharing, and alignment with organizational standards.
Ensure data quality, integrity, and security through validation, monitoring, and governance practices.
Maintain and evolve data models, schemas, and metadata for analytics and reporting.
Designs, develops, and deploys scalable reporting and dashboard solutions to support business intelligence, data visualization, and operational efficiency, ensuring appropriate security measures.
Troubleshoot and resolve data issues, ensuring high availability and performance of data systems.
Integrate data from a variety of business systems including ERP, SharePoint, external systems.
Design, implement, and maintain secure cross-region data integration processes to extract data from remote databases in China and Turkey, validate and transform it, and consolidate it into the U.S.-based ERP database while ensuring data integrity, reliability, performance, and compliance with organizational security standards.
Optimize SQL queries, stored procedures, indexes, database objects, and data-loading processes to improve performance, scalability, and resource utilization.
Monitor scheduled data pipelines, database jobs, and integration processes; implement logging, alerting, retry mechanisms, and exception handling to identify and resolve failures promptly.