Data Analysis Team Lead

What you will own:

• Turn ad-hoc work into a clear, documented and measured delivery process.
• Grow the team's seniority through clear grades, development plans and mentoring.
• Make sure every result is validated and answers the right business question before it reaches stakeholders.
• Bring engineering practices to industry standard: version control, code review, CI/CD and automated data tests for all data work.
• Make AI a safe, everyday tool of the team.
• Evolve the data platform architecture and take on the hardest technical problems.

Responsibilities:

Assessment and roadmap:

• Audit the team, its processes and the data platform; find bottlenecks and improvement steps.
• Build a roadmap of changes (what we try, adopt or drop) with quick wins and measurable targets; agree it with management and review it regularly.

Processes and their control:

• Define and document standards: lifecycle of an analytics request (intake → requirements → delivery → acceptance), Definition of Done, templates for requirements and reports, versioning and documentation rules.
• Optimize intake, prioritization and estimation; build a catalog of typical requests with standard solutions.
• Control execution through regular reviews and process metrics (lead time, rework rate, defects); adjust when needed.

Team development:

• Create or update the competency and grade matrix (Junior → Middle → Senior → Lead) with clear promotion criteria. Build an individual development plan (IDP) for each team member and review it regularly.
• Run 1:1s, give regular structured feedback, take part in performance reviews together with the resource manager; handle underperformance.
• Launch mentoring and knowledge sharing: internal demos, pair work, reviews as a learning tool.
• Take part in hiring: position profiles, interviews, onboarding.

Solution validation and quality:

• Introduce mandatory peer / lead review of analytical results before they go to stakeholders.
• Check that the analysis answers the right business question, not just "calculates numbers".
• Find inefficiencies (manual routine, duplication, repeated requests) and remove them through automation and standardization.

Architecture and engineering practices (hands-on):

• Own the target architecture of the data lake and reporting; introduce a layered data model (raw → cleansed → business-ready) with consistent naming, documentation and ownership.
• Put all code under version control with code review; build CI/CD and automated data tests; introduce Infrastructure as Code.
• Choose and introduce modern tools where the assessment shows real value (e.g. dbt, orchestration, data quality tools).
• Optimize performance and cost of queries and pipelines.
• Take the most complex tasks yourself, across analysis, data engineering and visualization.

AI and market trends:

• Track trends in analytics: AI assistants, LLMs for analysis and insight generation, self-service BI, augmented analytics, data storytelling.
• Run pilots of AI tools, measure their effect (time saved, quality, risks) and scale what works.
• Set rules for safe AI use: data confidentiality, validation of AI outputs, accountability for conclusions.
• Raise the team's AI literacy: training, internal guides, prompt libraries, best practices; work with the company's AI Adoption owner.
• Present changes to management and stakeholders, justify investments in new tools.

Opportunities and clients:

• Take part in pre-sales: discovery, solution design, estimates, PoCs for data and analytics opportunities.

Requirements:

Must have:

• 5+ years in data analytics, data engineering or BI, with hands-on experience in all three: analysis, data pipelines, visualization.
• 2+ years leading a team of 4+ people: 1:1s, feedback, performance, hiring.
• Real experience building or rebuilding team processes, with concrete "before → after" examples.
• Concrete stories of growing specialists: who, from which level, in what time, thanks to what.
• Expert SQL; solid Python for data work.
• Hands-on experience with AWS data services (S3, Athena, Glue, Lambda) or their equivalents in another cloud.
• Strong data modeling: dimensional modeling, layered data architecture.
• Version control, code review and CI/CD for data in practice.
• Strong skills in at least one BI tool (Power BI preferred).
• Critical thinking: can take apart someone else's solution and find logical and methodological errors.
• Personal, practical use of AI in analytics work (not just general awareness), and examples of bringing a new tool or approach into a team, including overcoming resistance to change.
• Experience working with different stakeholders, including top management.
• English B2+ (client communication).

Nice to have:

• dbt or another transformation framework in production.
• Infrastructure as Code (Terraform, AWS CDK); orchestration (Airflow, Step Functions).
• Data quality frameworks (dbt tests, Great Expectations, Soda).
• LLM APIs inside data pipelines (e.g. Amazon Bedrock); text-to-SQL, semantic layer.
• Life sciences / pharma or marketing / omnichannel analytics.
• AWS certifications; pre-sales or consulting experience.

Personal qualities:

• Proactivity, ownership, structured thinking, confident communication.
• Leads and influences without formal authority.
• Coaching style; can give difficult feedback and work with underperformance.
• Emotional intelligence; keeps motivation and trust in the team.
• Balanced view of AI: enthusiasm plus understanding of its limits and risks.
• Habit of continuous learning: communities, conferences, courses, experiments.