At McCormick, we bring our passion for flavor to work each day. We encourage growth, respect everyone's contributions and do what's right for our business, our people, our communities and our planet. Join us on our quest to make every meal and moment better.
Founded in Baltimore, MD in 1889 in a room and a cellar by 25-year-old Willoughby McCormick with three employees, McCormick is a global leader in flavour. With over 14,000 employees around the world and more than $6 Billion in annual sales, the Company manufactures, markets, and distributes spices, seasoning mixes, condiments and other flavourful products to the entire food industry, retail outlets, food manufactures, food service businesses and consumers.
While our global headquarters are in the Baltimore, Maryland, USA area, McCormick operates and serves customers from nearly 60 locations in 25 countries and 170 markets in Asia-Pacific, China, Europe, Middle East and Africa, and the Americas, including North, South and Central America
Position Overview
As a Data Engineer at McCormick, you will play a pivotal role in the build and delivery of data products from simple to complex and supporting McCormick business units with their data and analytics needs.
Your responsibilities will include delivering and supporting data for existing analytics solutions, tooling, and solutions, researching new features and implementing automations. You will support business users, Data Scientists and Data Analysts to convert business expectations into data products and data models usable by business to deliver AI, analysis, reporting, and data-driven recommendations to stakeholders and executives.
This role will be accountable for building and maintaining scalable data pipelines from source systems. The Data Engineer will ensure the availability, reliability, and performance of data products by integrating raw data from various sources. Key responsibilities include data modeling, ETL (Extract, Transform, Load) development, and ensuring data quality and security. This role will be accountable for data coming in from 1-3 source systems.
Key Responsibilities
1. Execute
2. Data Extraction, Load and Transformation
- Implement and maintain 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.
- Support the development and maintenance of 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.
3. Process Improvement, Performance and Cost optimization tuning
- Collaborate with Data Science, AI, and Data product teams to optimize performance and cost effectiveness of their solutions.
- Identify and support the design of internal process improvements, including automating manual processes, optimizing data product delivery, and redesigning solutions for enhanced scalability.
- Implement solution adjustments to improve performance and cost-effectiveness of data products.
4. Issue Resolution and Support
- Assist stakeholders with data-related product pipeline issues and support their data product needs.
- Work with the Analytics Operational Support team to investigate, troubleshoot, and resolve data errors / discrepancies.
Required Qualifications:
Level of Education and Discipline
- Bachelor’s degree in Mathematics, Statistics, Computer Science, Data Analytics/Science, or related field
Certifications and/or Licenses
- Microsoft Certified: Azure Data Engineer Associate (DP-900) or Microsoft Certified: Fabric Data Engineer Associate or related cloud technologies, Fabric IQ/databricks certifications a plus
Experience
- 3-5 years of data engineering experience.
- Demonstrated ability coding in one or more languages (PySpark preferred).
- Experience with building data pipelines.
- Demonstrated ability to manage multiple priorities simultaneously.
- Basic SQL performance tuning.
- Structured + semi-structured data handling.
- Unit testing for pipelines.
- Git-based workflow.
- Exposure to medallion architecture.
- Understanding of data privacy basics.
Interpersonal Skills
- Effective communication skills and ability to communicate effectively on technical and business issues both internally and externally.
- Ability to work independently, navigate problems, resolve conflicts, and bring solutions to the table.
- Build strong interpersonal relationships
- Time management and prioritization skills
- Strong technical curiosity and passion for problem solving and innovation
- Ongoing aspiration to learn about new industry tools
Other Skills & Competencies
- 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 Factory, Azure ML, Azure DevOps (for CI/CD), OR (Secondary)
- GCP BigQuery, FiveTran, GCP Cloud Composer, GCP DLP (Data Loss Prevention), GCP Cloud Run, Vertex AI etc
At McCormick, we have over a 100-year legacy based on our “Power of People” principle. This principle fosters an unusually dedicated workforce requiring a culture of respect, recognition, inclusion and collaboration based on the highest ethical value
