Spotfire Data Engineer

Job Tittle : Spotfire Data Engineer

Analyze and understand data from multiple source systems independently,

including data structure, business meaning, quality constraints, and reporting

requirements.

2. Collaborate with business stakeholders, data owners, analysts, and end users

to gather requirements, clarify reporting needs, validate outputs, and ensure

that solutions meet business objectives.

3. Design, build, and maintain Spotfire applications, dashboards, and self-

service analytical tools that provide intuitive, accurate, and actionable insights.

4. Develop Spotfire features including calculated columns, custom expressions,

data functions, filters, markings, property controls, action controls, information

links, and guided workflows.

5. Translate business requirements and logic into technical specifications,

mapping documents, data models, and implementation plans.

6. Design and implement scalable data models, tables, views, and reusable

datasets to support reporting, analytics, and application development.

7. Write efficient SQL, Python, PySpark, and Spark SQL scripts to ingest,

transform, validate, and prepare data for Spotfire applications and other

analytical tools.

8. Create and maintain data pipelines and orchestration workflows using Azure

Databricks, Azure Data Factory, and related enterprise data platforms.

9. Perform data quality checks, reconciliation, and validation to ensure accuracy,

completeness, consistency, and reliability of reports and applications.

10. Conduct functional testing, regression testing, user acceptance testing

support, and issue resolution before deployment or release.

11. Optimize queries, data models, and Spotfire application performance to

improve load times, user experience, scalability, and maintainability.

12. Provide operational support for existing Spotfire applications and data tools,

including troubleshooting, root cause analysis, defect resolution, access-

related questions, and user support.

13. Maintain clear documentation of requirements, design decisions, data

mappings, workflows, test evidence, release notes, known issues, and

support procedures.

14. Collaborate with cross-functional teams to align data solutions with

compliance expectations, enterprise standards, security requirements, and

data governance principles.

15. Support change management activities by preparing user guidance, training

materials, communications, and knowledge transfer for new or enhanced

applications.

16. Identify opportunities to automate manual reporting activities, simplify

workflows, improve data usability, and increase reuse of trusted data assets.

17. Contribute to continuous improvement of development practices,

documentation standards, support processes, and overall data product

quality.

18. Perform related duties as assigned by the supervisor and maintain

compliance with all applicable company policies, procedures, and training

requirements.

Qualifications

Bachelor’s or Master’s degree in Computer Science, Information Systems,

Engineering, Data Analytics, Life Sciences, or a related technical discipline.

Required experience developing business intelligence applications, preferably

with strong hands-on experience in Spotfire.

Experience developing business intelligence applications and hands-on

experience in Tableau and Power BI are a surplus.

Strong knowledge of SQL and experience working with large, complex

datasets from multiple source systems.

Required experience with Python, PySpark, Azure Databricks, Azure Data

Factory, Azure Data Lake.

Ability to translate business requirements into practical, maintainable technical

solutions.

Strong analytical thinking, problem-solving skills, attention to detail, and ability

to investigate data or application issues independently.

Good communication skills and ability to work effectively with both technical

and non-technical stakeholders.

Experience working in a regulated, quality, pharmaceutical, clinical, or GxP-

related environment is an advantage.

Key Competencies

Customer focus and ability to understand business context.

Ownership mindset with accountability for quality, timelines, and sustainable

delivery.

Structured problem solving and ability to manage ambiguity.

Clear communication, documentation, and stakeholder management.

Continuous improvement mindset with a focus on simplification, automation,

and reuse.

Commitment to compliance, data integrity, and secure handling of information.