is an AI-native Intelligent Experiences Enabler for life sciences, helping pharma and biotech organizations move from fragmented content operations to connected, compliant, and measurable omnichannel engagement with HCPs and patients. By combining proprietary technology — eWizard, eVa, Kheiron, and Lexi — with agentic AI and strategic consulting, we support the full content lifecycle: from creation and MLR validation to localization and omnichannel publishing. Our clients include Top 50 life sciences, pharma, biotech, and medtech companies around the globe. 

• 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.

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.

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.