Data Analysis Team Lead
New
V
VisevenLife sciences analytics
Listing location: Bucharest; Workplace type: Remote; Structured job location: BucharestFull-TimeLead
Salary not disclosed
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Job Details
- Languages
- English B2+ (client communication)
- Experience
- 5+ years in data analytics, data engineering or BI; 2+ years leading a team of 4+ people
- Required Skills
- PythonSQLMicrosoft Power BICI/CDData visualizationData modeling
Requirements
- Have 5+ years of experience in data analytics, data engineering or BI.
- Have hands-on experience in analysis, data pipelines and visualization.
- Have 2+ years leading a team of 4+ people, including 1:1s, feedback, performance management and hiring.
- Have experience building or rebuilding team processes, with concrete before-and-after examples.
- Demonstrate experience developing specialists, including examples of their progression.
- Bring expert SQL skills and solid Python skills for data work.
- Have hands-on experience with AWS data services (S3, Athena, Glue, Lambda) or equivalent services in another cloud.
- Have strong data modeling skills, including dimensional modeling and layered data architecture.
- Have practical experience with version control, code review and CI/CD for data.
- Have strong skills in at least one BI tool; Power BI is preferred.
- Bring critical thinking skills to identify logical and methodological errors in analytical solutions.
- Have practical experience using AI in analytics and introducing a new tool or approach into a team.
- Have experience working with stakeholders, including top management.
- Have English proficiency at B2+ for client communication.
Responsibilities
- Audit the team, its processes and the data platform; identify bottlenecks and improvement steps.
- Build and regularly review a roadmap of changes with quick wins and measurable targets.
- Define and document analytics request, delivery, acceptance, versioning and documentation standards.
- Optimize intake, prioritization and estimation, and monitor execution through reviews and process metrics.
- Create or update competency and grade matrices, individual development plans, and regular team feedback and mentoring practices.
- Participate in hiring, including position profiles, interviews and onboarding.
- Introduce peer or lead review of analytical results and check that analyses answer the relevant business question.
- Own the target architecture for the data lake and reporting, including a layered data model.
- Implement version control, code review, CI/CD, automated data tests and Infrastructure as Code; optimize query and pipeline performance and cost.
- Pilot AI tools, assess their effects and risks, establish safe-use rules, and support team AI literacy.
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