Senior Data Analyst II

About the job

Sr Data Analyst II (Content Flow Analytics)

Would you like to be part of a team that delivers high-quality software to our customers?

Are you a highly visible champion with a ‘can do’ attitude and enthusiasm that inspires others?

About our Team:- Elsevier Health applies innovation, facilitates insights, and helps drive more informed decision-making for our customers across global health. We support health providers by providing accessible, trusted evidence-based information; prepare more medical and nursing students with effective tools and resources; provide insights that help clinicians improve patient outcomes; and supports a more personalized and localized healthcare experience. All for the benefit of every patient.

About the Role: -

The Senior Data Analyst II leads the end-to-end delivery of analytics and reporting that improve content-flow performance , strengthen data-driven decision making , and accelerate continuous improvement across global Operations. This role operates with a high degree of independence—translating ambiguous business questions into structured hypotheses, building the right data products (dashboards, KPI frameworks, deep-dive analysis), and influencing senior stakeholders through clear, story-led insights. Publishing domain exposure is preferred (not mandatory); the role is expected to bring/build a strong understanding of the content journey and operational handoffs, including linked data (CP/LD). (e.g., Content production/management interfaces, supplier touchpoints, workflow steps, quality gates, exceptions/rework loops, throughput and cycle time drivers). The role partners closely with Operations leadership and adjacent teams (e.g., Strategy, Content production operations teams, Finance, Analytics CoE, IT/ Data Warehousing teams) to ensure consistent metrics, trusted data foundations, and measurable operational outcomes. A Senior Data Analyst II is a recognised analytics expert within their domain, increasingly shaping not just outputs but also how the organisation thinks about metrics, quality, and performance management . They coach junior analysts and enable business users to adopt reporting correctly and consistently.

Responsibilities: 1) Stakeholder Partnership and Insight Leadership

- Lead analytics engagements fully independently: clarify objectives, frame hypotheses, define success measures, and drive execution end-to-end.

- Act as a thought partner to senior stakeholders—guiding them through hypothesis thinking, prioritising the most meaningful metrics, and shaping decisions using evidence.

- Proactively identify problem areas in the content journey (bottlenecks, defect leakage, rework, delay drivers, supplier variability, workload imbalance) and propose targeted analyses and corrective actions.

- Present insights and recommendations in compelling, non-technical narratives—tailored to stakeholder context, decision points, and operational realities.

- Support senior leaders through decision cycles by offering options, trade-offs, impact sizing, and monitoring plans.

2) Reporting, Dashboards, and KPI Delivery

- Ensure delivery of reports/dashboards/BI structures that meet business requirements and support ongoing trend and performance analysis.

- Define “best-fit” reporting solutions (what to build, why it matters, and how it will be used), including:

- KPI definitions and levels (team/manager/function/global views)

- metric logic, filters, segmentation, and benchmarking approach

- adoption plan to embed usage into BAU operations

- Deliver accurate and timely KPI reporting across all relevant stakeholder levels, enabling clear identification of performance focus areas.

- Partner with Data Management to publish trusted dashboards to Operations users and senior governance forums with consistent definitions and strong data quality controls.

3) Publishing Domain and Content-Flow Process Analytics

- Apply deep understanding of publishing/content operations to interpret workflow behaviour and identify improvement opportunities.

- Build and maintain measurement frameworks for content flow, such as:

- throughput and cycle time analysis (lead time, queue time, touch time)

- stage-level performance and handoff quality

- exception/rework drivers, defect trend analysis, root cause themes

- supplier performance variation and operational risk hotspots

- productivity, capacity, and workload distribution insights

- Collaborate with process owners to convert operational pain points into measurable indicators and actionable improvement levers.

4) Data Preparation, Integration, and Engineering Literacy

- Handle large and complex datasets; combine data from multiple systems and sources using SQL and modern analytics tooling.

- Demonstrate strong understanding of data integration mechanics and data engineering concepts (pipelines, transformations, joins, grain, lineage, refresh cadence, performance constraints).

- Work effectively with big data platforms and tools (Databricks preferred) and demonstrate fluency in:

- SQL (essential)

- Python (desirable) and/or Scala (desirable, not essential)

- additional tooling such as / similar to KNIME, Tableau/Power BI, and warehouse technologies (Snowflake exposure is an advantage)

- Act as a bridge between business needs and technical implementation—ensuring solutions are scalable, maintainable, and aligned to platform standards.

5) Advanced Analytics, Modelling, and Statistical Thinking

- Undertake complex analyses using statistical and analytical techniques appropriate to the problem, including:

- trend decomposition, segmentation, and cohort analysis

- outlier/anomaly detection and exception monitoring

- hypothesis testing, correlation/association exploration, scenario modelling

- Develop (or continue developing) advanced analytics capabilities such as predictive analytics, basic machine learning applications, and NLP use cases where relevant to content operations.

- Translate modelling outputs into operational recommendations, guardrails, and monitoring metrics (not just model accuracy).

6) Data Governance, Quality, and Metric Consistency

- Ensure governance for quality, integrity, completeness, timeliness, and reliability so reporting is trustworthy.

- Carry out regular data quality assessments in new and existing reporting solutions; identify and resolve gaps and discrepancies.

- Coordinate with cross-functional teams to align on metric definitions and eliminate inconsistencies across dashboards and stakeholder reporting.

- Lead (or co-lead) documentation practices: data definitions, KPI logic, reporting change logs, refresh schedules, and known limitations.

- Act as an influencer for data quality culture—motivating stakeholders to treat data trust as foundational to performance management.

7) Coaching, Enablement, and Analytics Community Leadership

- Coach and support junior analysts: data preparation, modelling approaches, visualization standards, stakeholder management, and project execution.

- Provide training and ongoing support to reporting champions and business users to ensure consistent and effective reporting usage.

- Influence and support fellow analysts with problem resolution, documentation, and best-practice adoption.

8) Project and Program Contribution

- Lead medium/tactical analytics projects, managing tasks, blockers, timelines, and stakeholder expectations with minimal oversight.

- Participate in international programs and projects requiring analytics/BI expertise; put metrics in place to monitor success criteria and guide delivery decisions.

- Provide detailed analysis and documentation to support project planning and resourcing; enable automation wherever feasible.

- Represent the analytics function in governance discussions and ensure decisions are communicated clearly to the wider reporting community.

Requirements (Qualifications & Experience) Education

- Bachelor’s degree required; preference for quantitative disciplines (Economics, Math, Statistics, Data Science, Engineering, or similar).

- Master’s/PhD is a plus (particularly for modelling-heavy work).

Experience

- Typically, 5–8 years in data analytics / business analytics, ideally within a global multinational environment.

- Preference for demonstrated exposure to publishing operations and content-flow processes or equivalent process-heavy operations domains.

Core Skills (Essential)

- Requirements gathering and stakeholder management; ability to elicit clear and accurate reporting needs.

- Advanced analytical skills with strong attention to detail and accuracy.

- Strong SQL skills and comfort working with large datasets.

- High proficiency in visualization/reporting tools (Tableau and/or Power BI).

- Excellent communication and presentation skills—able to tell clear, decision-oriented stories with data for non-technical audiences.

- Strong documentation discipline (definitions, logic, change tracking).

Desirable Skills

- Databricks experience (preferred).

- Python and/or Scala exposure (desirable, not essential).

- Statistical modelling exposure and ability to apply appropriate techniques.

- Snowflake experience (advantage).

- Experience with KNIME or similar tooling.

- Familiarity with data warehousing and modern analytics architectures.

Functional Competencies (Expected Level)