Manager, IT Enterprise Data Warehousing

Key Responsibilities:

Inspire and Lead High-Performing Teams:

• Cultivate a culture of trust, collaboration, and accountability by mentoring team members, setting clear goals, and fostering professional growth across diverse technical and business functions.
• Foster an environment of continuing education and career development for team members that includes both internal and external relationship building and understanding of business operations.
• Manage daily team operations, including task assignments, workload balancing, and performance monitoring.
• Conduct regular team meetings to review progress, address challenges, and align priorities.
• Provide regular updates to leadership on team status.

Drive Clear and Effective Communication: 

• Act as a central communication hub between technical teams, business stakeholders, and executive leadership—translating complex technical concepts into actionable business insights and aligning priorities across the organization.
• Communicate project status, timelines, and outcomes clearly and effectively.
• Communicate system updates, changes, and benefits to stakeholders at all levels of the organization.

Business Alignment:

• Implementation of business system improvements that support organizational goals, ensuring initiatives are well-communicated, properly resourced, prioritized, and effectively executed.

Project and Change Management:

• Guide teams and stakeholders through change management processes with empathy and clarity, promoting user adoption and minimizing disruption through proactive communication and training.
• Facilitate requirements gathering sessions and translate business needs into actionable analytics initiatives.
• Develop project plans, allocate resources effectively, and coordinate with internal and external teams and stakeholders.

Analytics Strategy & Oversight:

• Work with leadership to align and evolve team capabilities in line with the strategic goals of the organization.
• Guide the development and deployment of data pipelines, ETL processes, and validation methods for the delivery of curated datasets to be consumed by our internal stakeholders building reports and advanced analytics solutions for business insights.
• Aligning with the enterprise data strategy, ensure consistency in data modeling, visualization standards, and documentation across the team.
• Promote alignment with the overall enterprise data strategy and industry best practices in analytics design, performance optimization, and user experience.

Data Governance & Quality Assurance:

• Support the Data Governance team and data governance initiatives by enforcing standards for data integrity, security, and compliance.
• Collaborate with IT, compliance, and data governance teams to ensure adherence to regulatory requirements (e.g., GDPR).
• Monitor and improve data quality processes, including validation, cleansing, and enrichment.

Innovation & Advanced Analytics:

• Encourage the exploration and adoption of emerging technologies and methodologies (e.g., AI, machine learning, cloud analytics).
• Evaluate the impact of analytics solutions and recommend enhancements.

Documentation & Training:

• Ensure comprehensive documentation of BI solutions, processes, and governance protocols.
• Promote data literacy and provide training to stakeholders on BI tools and data interpretation.

Vendor Relationship Management: 

• Act as the primary point of contact for external vendors and consultants.
• Manage relationships with software vendors, ensuring optimal service delivery and adherence to contracts.
   

Key Skills:

Data Governance and Quality Assurance:

• Understanding of data privacy, compliance regulations (e.g., GDPR, CCPA), and best practices for data management.
• Ability to implement data quality frameworks and ensure accuracy and consistency in datasets.

Project Management:

• Proficiency in Agile methodologies, project planning, and task prioritization.
• Experience with project management tools like Planisware, Jira, MS Project.

Team Leadership and Collaboration:

• Ability to hire, manage and mentor a team of data engineers and architects.
• Strong interpersonal skills to collaborate with cross-functional teams, including business stakeholders, developers, and data scientists.

Problem-Solving and Critical Thinking:

• Strong analytical mindset to troubleshoot data pipeline issues or optimize workflows.
• Ability to devise innovative solutions to complex data problems.

Communication and Storytelling:

• Exceptional verbal and written communication skills to explain technical concepts to non-technical stakeholders.
• Ability to translate data insights into actionable business strategies.

Data Engineering Expertise:

• Hands-on experience building scalable data pipelines and orchestration using tools like Azure Synapse, Azure Data Factory, Microsoft Fabric, and SAP Data Services.
• Deep familiarity with ETL design patterns, data transformation best practices, incremental loading, schema evolution, and performance tuning.
• Experience with PySpark and Spark SQL for ETL and large-scale data transformations.

Databases & Warehousing:

• Strong knowledge of relational databases (e.g., SQL Server, Oracle, SAP HANA).
• Experience with cloud-based data warehouses including Azure SQL DB, Fabric Lakehouse, Snowflake, Databricks, Google BigQuery, or Amazon Redshift.

Big Data and Cloud Platforms:

• Practical experience deploying and operating data solutions on major cloud platforms  (AWS, Azure, Google Cloud) with attention to cost, scalability, and security.

Data Analytics and Visualization:

• Proficient in SQL and Python for data analysis, modeling, and automation.
• Experience with visualization tools such as Power BI, Qlik, SAC, Business Objects, or Tableau for presenting insights effectively.

Programming and Scripting:

• Ability to write scripts and automation for deployment, monitoring, and operational tasks to improve reliability and reduce manual effort.
• Experience implementing logging, alerting, testing, and data validation checks across pipelines and reporting layers to maintain SLAs and trust in data.
   

Other Qualifications:

• Bachelor’s Degree in Computer Science, Information Systems, Business Analytics, or related discipline is required.
• 5+ years of experience in business intelligence, data analytics, or data science, with a proven ability to lead enterprise analytics initiatives.
• 5+ years of technical experience with data modeling, SQL, Power BI, Azure Synapse, Fabric, SAP SAC, SLT, BODS, Datasphere for ETL preferred.
• Project management experience required.