Lead Data Engineer Full Time NEW

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 Lead 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 5-10+ source systems.

Key Responsibilities

• Plan, Design, and Execute
• Partner with data product managers to deliver annual data roadmaps.
• Accountable for ETL designs across data sources.
• Execute ETLs across data sources.
• Develop integration approach for data products to support AI and advanced analytics.
• Establish best practices for medallion architecture (bronze/silver/gold layers), data lineage, and performance optimization.
• Data Extraction, Load, and Transformation
• Design, build and oversee pipelines ingesting all internal (SAP S/4HANA, legacy ERP, CRM, manufacturing, supply chain, HR) and external data sources (market data, retail scanner data, syndicated data, partner APIs, IoT feeds).
• Oversee the design of complex pipelines, setting the direction and standards.
• Ensure all data products feed off of lakehouse data that has SAP-embedded data characteristics.
• Standardize reusable ingestion frameworks, metadata-driven pipelines, and CI/CD-enabled deployment patterns.
• Evaluate & define suitable tools (e.g., Kafka, Sqoop, Fivetran, custom Python/Scala scripts) for optimal extraction.
• Data quality and governance
• Establish validation checks during extraction and load (schema enforcement, deduplication, anomaly detection) and ensure data classification alignment, enforcement & audit traceability.
• Implement monitoring and alerting systems to catch pipeline failures early.
• Enforce regulatory compliance (e.g., GDPR, PDPA).
• Manage PII across systems and workflows
• Driving innovation and strategy
• Drive automation in pipeline deployment (CI/CD for data workflows).
• Review and recommend emerging tools (dbt, Airbyte, Delta Lake) to modernize extraction and load processes.
• Champion cloud-native solutions for scalability and cost efficiency.
• Issue Resolution and Support
• Monitor and troubleshoot the data pipelines proactively across data sources.
• Provide expert-level support and guidance to data teams across the Enterprise.

Required Qualifications

Level of Education and Discipline

• Bachelor’s degree in Mathematics, Statistics, Computer Science, Data Analytics/Science, or related field; Masters degree a plus

Certifications and/or Licenses

• Microsoft Certified: Azure Data Engineer (DP203, Advanced Architecture) or Microsoft Certified: Fabric Data Engineer Associate or related cloud technologies, Fabric IQ/Databricks certifications a plus

Experience

• 8+ years of data engineering experience.
• Demonstrated ability coding in one or more languages (PySpark preferred).
• Experience with building data pipelines.
• Experience with knowledge graphs.
• Demonstrated ability to manage multiple priorities simultaneously.
• Data contracts implementation.
• Cross-domain integration patterns.
• Enterprise data modeling standards.
• Advanced Spark optimization.
• Architectural decision records (ADR ownership).

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 and be able to persuade, negotiate and influence for meeting business objectives.
• Innovative thinker – able to turn customer requirements into workable solutions
• Excellent 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