01Key Responsibilities
Plan and Design
Collaborate with data product managers to gather data product requirements.
Design end-to-end solutions including data security, data quality and performance requirements.
Prepare documentation and with data product managers to define the implementation plan.
Data Extraction, Load and Transformation
Implement ELT pipelines to efficiently ingest and transform data from a wide variety of data sources and deliver datasets that meet business requirements.
Ensure efficient and reliable data mapping to support business needs.
Deliver complete documentation and knowledge transfer sessions for the Team and business partners.
Maintain existing solutions, implement optimizations and enhancements, monitor data quality.
Develop and maintain scalable data pipelines leveraging Azure Synapse, PySpark, APIs, and SQL & performing advanced data cleaning, transformation, and manipulation to ensure high-quality, and reliable data flows.
Process Improvement, Performance and Cost optimization tuning
Collaborate with Data Science, Machine Learning and Business Analytics teams to optimize performance and cost effectiveness of their analytics solutions.
Identify and design internal process improvements, including automating manual processes, optimizing data delivery, and redesigning solutions for enhanced scalability. Work with Azure Analytics Product Owner to prioritize and schedule implementation.
Design and implement existing solution adjustments to improve performance and cost-effectiveness.
Suggest and introduce best practices for Data and AI engineering.
Issue Resolution and Support
Assist stakeholders with data-related technical issues and support their data needs. Work with the Analytics Operational Support team to investigate, troubleshoot, and resolve data errors / discrepancies.
Provide expert-level support and guidance to data teams across the Enterprise.
People Leadership & Capability Building
Lead, mentor, and develop a high-performing team of 8-10 data engineers.
Define technical standards, code review practices, and engineering excellence frameworks.
Build career paths, upskilling programs, and succession plans within data engineering.
Foster a culture of innovation, accountability, collaboration, and continuous learning.
Be accountable for building a winning data engineering team driven by making data a core asset for McCormick.
Desired Candidate Profile:
Bachelors degree in Mathematics, Statistics, Computer Science, Data Analytics/Science, or related field; Masters in technical field or MBA a plus Microsoft Certified: Azure Data Engineer (DP203+AZ305) or Microsoft Certified: Fabric Data Engineer Associate or related cloud technologies, Fabric IQ/databricks certifications a plus 10+ years of data engineering experience. Demonstrated ability coding in one or more languages (PySpark preferred). Experience with data visualization software (Power BI preferred). Experience with building data pipelines. Experience with knowledge graphs. Demonstrated ability to manage multiple priorities simultaneously. Knowledge of data analysis, visualization techniques, and frameworks. Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement. Experience with the following tooling: SQL, Fabric, Databricks, Synapse, Azure Data .
