Data Engineering Manager

• Lead the Data Foundation initiative to modernize the enterprise data ecosystem through a scalable lakehouse architecture and cloud-based data platform capabilities • Define the data engineering strategy, target architecture, and reusable pipeline frameworks needed to deliver governed, high-quality data products • Develop the strategy for a Data Supermarket that delivers business-ready data products for use across multiple functions. • Translate complex business requirements and technical challenges into scalable architecture decisions and executable delivery plans • Provide technical leadership for business separation activities, ensuring alignment to future-state operating models and platform continuity • Establish and enforce best practices for: Data modeling (dimensional, Data Vault 2.0) • Pipeline design, modularity, and reuse • Engineering standards and quality controls • Establish and scale data governance, data quality, and observability practices, including monitoring, lineage, reliability, and service-level expectations • Define and implement automated testing strategies for data pipelines, including validation, data quality controls, and CI/CD integration • Lead development and orchestration using Databricks, Python, SQL, dbt, Airflow, and cloud-native tools • Partner with vendors and internal teams to manage delivery, enforce standards, and drive outcome-based execution • Collaborate across Product, Supply Chain business, AI/ML, Data Governance, and IT teams to deliver measurable business impact Requirements Minimum of 8 years of experience in data engineering, including 2 or more years in leadership or people management roles Strong hands-on technical experience with: Databricks, PySpark, Spark SQL Python and SQL development Cloud platforms (Azure preferred, AWS acceptable) Proven expertise in: Modern data architecture and platform design Dimensional modeling and Data Vault 2.0 Experience with: dbt, Airflow, Azure Data Factory (or equivalent tools) CI/CD pipelines, automation frameworks, and testing practices Required experience designing and implementing automated testing strategies for data engineering pipelines, including: End-to-end pipeline validation Data quality and integrity testing Integration with CI/CD pipelines Experience leading distributed teams and partnering effectively with external vendors and cross-functional stakeholders Core Competencies Demonstrates expertise in modern data architecture, data engineering strategy, and scalable lakehouse architecture, with a strong focus on data quality, governance, and automated testing practices. Proven ability to lead cross-functional teams and deliver high-quality data products that drive business impact. Highest-signal resume keywords Data Engineering Leadership Databricks Development Automated Testing Strategies Cloud Platform Expertise Data Governance Practices ATS Optimization Keywords Hard Skills Data Engineering Dimensional Modeling Data Vault 2.0 CI/CD Pipelines End-to-End Pipeline Validation Data Quality Testing Python Development SQL Development PySpark Spark SQL Soft Skills Technical Leadership Collaboration Stakeholder Management Problem Solving Communication Industry Keywords Data Ecosystem Data Governance Data Quality Data Products Business Separation Activities Tools & Technologies Databricks Dbt Airflow Azure Data Factory Cloud-Native Tools #J-18808-Ljbffr