Technical Data Engineering Manager

• Manage and coordinate workloads across multiple projects, ensuring efficient use of resources and timely delivery.
• Oversee the engineering work delivered by vendors, maintaining quality and performance standards.
• Lead and mentor a team of data engineers, promoting professional growth and development.
• Lead the team in developing technical solutions that meet business expectations and meet defined development standards and best practices
• Ability to apply a continuous improvement mindset towards designing more effective solutions, building the technical vision for data engineering and making recommendations for the technical needs.
• Collaborate with Data Intelligence team members and business stakeholders to define project requirements, objectives, and timelines.
• Ensure development and adherence to best practices in data engineering and technology.
• Monitor and report on project progress, risks, and issues to the Director of Data Intelligence and Technology.
• Implement and optimize data engineering processes, tools, and methodologies.

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• MUST: 4+ years with data modeling and ETL development.
• 4+ years with data processing and querying technologies.
• 4+ years with data integration technologies.
• At least 4 years of experience leading a team, preferably multi-location/time zone
• Proven experience in managing workloads across multiple projects
• Strong knowledge and experience with SAP, MS Fabric, Azure Synapse, and/or Azure Data Factory.
• Excellent leadership and team management skills.
• Strong problem-solving and analytical abilities.
• Outstanding communication and interpersonal skills.

• Experience with cloud-based data solutions and big data technologies.
• Knowledge of data governance and compliance standards.
• Experience in Agile and Scrum methodologies.
• For a Technical Data Engineering Lead, you should consider including the following specific technical skills:

• Data Warehousing: Experience with designing, implementing, and maintaining data warehouses.
• ETL Processes: Proficiency in Extract, Transform, Load (ETL) processes and tools such as Apache NiFi, Talend, or Informatica.
• Big Data Technologies: Familiarity with big data technologies like Hadoop, Spark, and Kafka.
• Database Management: Strong knowledge of SQL and NoSQL databases, including MySQL, PostgreSQL, MongoDB, and Cassandra.
• Cloud Platforms: Experience with cloud platforms such as AWS, Azure, or Google Cloud, including services like Redshift, BigQuery, and Azure Data Lake.
• Programming Languages: Proficiency in programming languages such as Python, Pyspark Java, Scala, or R.
• Data Modeling: Skills in data modeling and schema design.
• Data Governance: Understanding of data governance principles and practices.
• Machine Learning: Basic knowledge of machine learning algorithms and frameworks can be beneficial.

Lubrizol