Data Analysis Manager
2 weeks ago
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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