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.