The Role
Lead analytics delivery across data engineering, reporting, and business intelligence. Develop SQL-based insights in Snowflake using dbt, maintain dashboards, monitor data quality, and translate complex questions into actionable conclusions. Own client deliverables, establish quality standards, manage priorities, and serve as an escalation point. Lead and mentor 3–4 analysts while partnering with stakeholders, Engineering, Product, and Operations to communicate findings and improve reporting infrastructure.
Summary Generated by Built In
About the Role
Key Responsibilities
Analytics & Data Acumen
- Work across structured and unstructured datasets to develop meaningful insights, identify trends, and support data-driven decision-making.
- Write and optimize complex SQL queries in Snowflake to extract, transform, and validate data; leverage DBT for modelling and pipeline management.
- Design and maintain dashboards and reporting frameworks that give stakeholders clear, reliable visibility into business performance.
- Translate ambiguous business questions into well-scoped analytical problems, driving them through to clear and defensible conclusions.
- Monitor key data streams and business dashboards, flagging and triaging anomalies or irregularities as they arise.
- Continuously look for opportunities to improve data quality, reporting logic, and analytical processes.
Client Deliverables & Quality Ownership
- Take end-to-end ownership of analytics deliverables — from scoping and analysis through to final output — ensuring accuracy, timeliness, and relevance.
- Review and quality-check team members' work before it reaches clients or stakeholders, upholding high standards of data integrity and presentation.
- Establish and enforce quality assurance processes and delivery standards across the team.
- Manage workload and priorities across the team to meet SLAs and client commitments consistently.
- Act as the escalation point for delivery risks, data issues, or quality concerns on active engagements.
Team Leadership & Development
- Lead, coach, and develop a team of 3–4 analysts, providing hands-on guidance and constructive feedback on their work.
- Foster a culture of curiosity, accountability, and continuous improvement within the team.
- Track team utilization and development needs, working with stakeholders to ensure effective allocation of capacity.
- Support analysts in building their technical skills and analytical thinking over time.
Stakeholder Collaboration & Relationship Building
- Build and maintain strong, trusted relationships with internal and external stakeholders — acting as a reliable analytics partner, not just a delivery resource.
- Engage proactively with stakeholders to understand their business context, anticipate analytical needs, and deliver insights that drive real decisions.
- Translate complex data findings into clear narratives tailored to both technical and non-technical audiences.
- Serve as the primary point of contact for analytics queries and deliverables, ensuring stakeholders are informed and confident in the outputs they receive.
- Collaborate with Engineering, Product, and Operations teams to align on data definitions, improve reporting infrastructure, and close gaps between data and business needs.
- Participate in stakeholder reviews, presenting findings and recommendations with clarity and confidence.
Requirements
Required Qualifications
- 6–9 years of experience spanning both data (engineering, operations, or pipelines) and analytics (reporting, insights, BI) disciplines.
- Strong SQL skills with hands-on Snowflake experience; familiarity with DBT for data modelling and transformation.
- Demonstrated experience owning and delivering analytics outputs to clients or senior stakeholders.
- Experience leading or mentoring a small team, reviewing analytical work, and maintaining delivery quality standards.
- Confident communicator — able to present data findings, manage stakeholder expectations, and build relationships at multiple levels.
- Strong analytical thinking with a structured approach to problem solving and root cause investigation.
- Experience with BI and dashboarding tools (e.g. Tableau, Looker, Power BI, or similar).
- Familiarity with project and task management tools such as Jira, Monday.com, or equivalent.
Preferred Qualifications
- Background in a centralized Analytics Centre of Excellence, shared services, or managed analytics environment.
- Familiarity with data quality frameworks, statistical methods, or anomaly detection techniques.
- Experience in fast-paced, client-facing, or operationally complex environments.
- (Optional) Experience with workflow automation tools or scripting (Python, or similar) to streamline repetitive analytical tasks.
- (Optional) Exposure to Generative AI or LLM-based tools in analytics workflows, reporting automation, or insight generation; familiarity with prompt engineering or AI-assisted analysis is a plus
Benefits
Perks and benefits:
- Competitive salary and performance-based bonuses.
- Comprehensive insurance plans.
- Collaborative and supportive work environment
- Chance to learn and grow with a talented team.
- A positive and fun work environment.
Skills Required
- 6–9 years of experience spanning data engineering, data operations, pipelines, analytics, reporting, insights, or business intelligence
- Strong SQL skills and hands-on Snowflake experience
- Familiarity with dbt for data modeling and transformation
- Experience owning and delivering analytics outputs to clients or senior stakeholders
- Experience leading or mentoring a small team and reviewing analytical work
- Experience maintaining delivery quality standards
- Strong communication and stakeholder relationship management skills
- Strong analytical thinking and structured problem-solving skills
- Experience with BI and dashboarding tools such as Tableau, Looker, or Power BI
- Familiarity with project and task management tools such as Jira or Monday.com
- Experience in an Analytics Centre of Excellence, shared services, or managed analytics environment
- Familiarity with data quality frameworks, statistical methods, or anomaly detection techniques
- Experience in fast-paced, client-facing, or operationally complex environments
- Experience with workflow automation tools or Python scripting
- Exposure to Generative AI or LLM-based tools in analytics workflows, reporting automation, or insight generation
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The Company
What We Do
Prescience is a Danish SaaS company that provides collaborative supply-chain execution software for industrial businesses. Its platform helps customers and suppliers plan production, track manufacturing progress, monitor execution, manage capacity, improve quality assurance, collect as-built documentation, and coordinate inventory and inbound and outbound logistics. By integrating supplier data in real time, Prescience delivers visibility, risk detection, and operational control across dispersed global supply chains.
