Data Analysis Manager

Roles and responsibilities

• Understanding of Data Analytics:
• Familiarity with data lifecycle: collection, storage, processing, analysis, and visualization.
• Knowledge of advanced analytics techniques such as predictive modeling, machine learning, and AI.
• Big Data and Cloud Platforms:
• Experience with tools like Hadoop, Spark, or Snowflake.
• Understanding of cloud platforms such as AWS, Azure, or Google Cloud.
• Data Integration and ETL:
• Awareness of ETL processes and tools like Informatica, Talend, or Apache NiFi.
• Database Management:
• Proficiency in working with relational (SQL) and non-relational (NoSQL) databases.
• Data Visualization:
• Familiarity with tools like Tableau, Power BI, or Looker.
• Cybersecurity and Compliance:
• Awareness of data security, privacy regulations (e.g., GDPR, CCPA), and governance best practices.
  

Certifications (advantage to have)

• Project Management:
• PMP (Project Management Professional)
• Certified Scrum Master (CSM)
• PRINCE2
• Data and Analytics:
• Google Data Analytics Certification
• AWS Certified Data Analytics - Specialty
• Microsoft Certified: Azure Data Engineer Associate
• Business Analysis:
• CBAP (Certified Business Analysis Professional)
• PMI-PBA (Professional in Business Analysis)
  

Experience

• Domain Expertise: Experience in the industry relevant to the analytics platform (e.g., finance, healthcare, retail).
• Technical Project Delivery: Previous involvement in implementing analytics platforms or similar technical projects.
• Cross-functional Team Leadership: History of working with multi-disciplinary teams, including engineers, analysts, and business units.
• Define Project Scope: Work with stakeholders to define the project’s objectives, deliverables, and expected outcomes, ensuring alignment with business goals.
• Data Strategy: Help shape the data strategy by advising on data collection, processing, and analytics methods.
• Develop Project Roadmap: Create detailed project timelines, milestones, and resource plans, ensuring that all project phases are accounted for and that progress is tracked.
• Risk Management: Identify and mitigate risks related to project scope, timeline, resources, or data quality that might impact project success.

Desired candidate profile

1. Project Management Expertise

• Project Management Methodologies: Knowledge of project management methodologies such as Agile, Waterfall, or Scrum to manage and adapt projects effectively.
• Time Management: Strong ability to manage timelines, set priorities, and meet deadlines while handling multiple tasks and responsibilities.
• Task Delegation: Ability to delegate tasks effectively, ensuring that team members are empowered to perform their best work.

2. Data and Analytics Knowledge

• Understanding of Data: Familiarity with data management practices, data quality, data governance, and data integration techniques.
• Advanced Analytics: Knowledge of machine learning, artificial intelligence, predictive modeling, and statistical analysis, although hands-on experience with coding is not typically required.
• Data Visualization: Proficiency with data visualization tools like Power BI, Tableau, or Looker to help present analytics results in an easily digestible format for stakeholders.

3. Stakeholder Management and Communication

• Communication Skills: Strong verbal and written communication skills to clearly explain complex data insights and analytics results to non-technical stakeholders.
• Stakeholder Alignment: Ability to manage and align diverse stakeholders, including business leaders, technical teams, and end-users, with the project’s goals and objectives.
• Influence and Negotiation: Skilled in influencing decisions and negotiating project scopes, timelines, and resources with stakeholders.

4. Analytical and Problem-Solving Abilities

• Critical Thinking: Ability to think critically and problem-solve, especially in situations involving large or complex data sets and unforeseen challenges.
• Decision-Making: Ability to make data-driven decisions and use analytics tools to optimize outcomes and mitigate risks.
• Data-Driven Approach: Focus on ensuring that analytics are actionable, helping the organization make decisions based on accurate, real-time data.

5. Leadership and Team Collaboration

• Team Leadership: Strong leadership skills to guide cross-functional teams, particularly in a fast-paced and often evolving environment.
• Motivating Teams: Ability to motivate and inspire teams, particularly data analysts, scientists, and engineers, to deliver high-quality results.
• Collaboration: Work collaboratively across departments and with different functional teams, balancing technical and business priorities.

6. Change Management and Adaptability

• Agile Adaptability: Ability to adjust project plans quickly in response to changing business priorities, new data findings, or technology changes.
• Flexibility: Comfort in working in a constantly evolving technological and business environment, with the ability to pivot or adjust strategies when needed.