Senior Data Product Developer

Position Overview: 

The Senior Data Product Developer is accountable for designing, building, testing, deploying, and supporting enterprise data products that enable trusted, scalable, and actionable business insights.

The role translates business needs into data solutions by developing data products, datasets, semantic layers, analytical models, pipelines, and reusable data assets that support decision-making, reporting, advanced analytics, AI, and operational processes.

The role manages data products throughout their lifecycle, including design, development, testing, deployment, maintenance, enhancements, optimization, recovery, and retirement. Working closely with business stakeholders, Product Owners, Data Engineers, Architects, Analysts, Data Scientists, Governance teams, and Technology partners, the role helps ensure data products are reliable, secure, well-governed, and deliver measurable business value.

Accountable for designing, building, deploying, and maintaining data products that transform enterprise data into trusted, scalable, and consumable assets that support business operations, reporting, analytics, AI, and decision-making while meeting enterprise standards for quality, governance, security, and performance.

Key Deliverables:

Design and develop enterprise data products, curated datasets, semantic models, and analytical data assets that address defined business needs. 

Translate business requirements, use cases, and operational challenges into scalable data solutions.

Build and maintain data pipelines, transformations, business logic, and data models using modern data platform technologies.

Prepare, transform, enrich, and validate data from multiple source systems to create trusted and reusable data assets.

Evaluate solution alternatives and technical trade-offs including scalability, performance, maintainability, usability, and cost.

Develop reusable frameworks, patterns, components, and metadata structures that accelerate data product delivery.

Document data product design, business rules, data lineage, transformations, assumptions, and technical decisions to support ongoing maintenance, governance, auditability, and knowledge transfer.

Integrate data products with enterprise data platforms, applications, dashboards, AI solutions, APIs, and operational processes.

Build automated validation, reconciliation, unit, integration, and end-to-end testing capabilities.

Implement and monitor data quality controls to ensure completeness, accuracy, consistency, timeliness, and reliability.

Assess outputs against defined business and technical acceptance criteria and address defects prior to release.

Deploy data products across development, testing, and production environments and manage releases throughout the lifecycle.

Develop and maintain automated build, testing, deployment, and operational processes.

Manage product versions, metadata changes, business rule updates, schema evolution, and retirement activities.

Ensure required testing, approvals, governance reviews, and readiness checks are completed before production releases.

Maintain deployment documentation, technical standards, operating procedures, and support documentation.

Monitor and optimize data product performance, scalability, availability, and cost as business needs evolve.

Investigate and resolve production incidents affecting data products and analytical solutions.

Execute approved production changes including deployments, enhancements, fixes, and performance improvements.

Support service restoration activities following approved incident and change management processes.

Participate in root cause analysis and implement improvements to data products, pipelines, controls, and operational practices.

Use stakeholder feedback, usage patterns, product adoption metrics, and business outcomes to continuously enhance data products.

 Experience and Qualifications: 

Bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Science, Mathematics, Statistics, or a related discipline.

Relevant certifications in Data Engineering, Snowflake, Databricks, Microsoft Fabric, Cloud Platforms, Analytics Engineering, or Data Governance are considered assets.

5+ years of experience designing, developing, testing, deploying, and supporting enterprise data products and analytical solutions.

Experience building and managing production-grade data pipelines, transformations, integrations, and semantic models.

Experience working with modern cloud-based data platforms and data architectures.

Experience implementing data quality, metadata, lineage, and governance capabilities.

Experience supporting production environments and troubleshooting operational issues.

Experience working within Agile product delivery teams.

Strong knowledge of data product lifecycle management, including design, development, deployment, maintenance, monitoring, enhancement, and retirement.

Strong understanding of data modeling, dimensional modeling, analytics engineering, data warehousing, and semantic layer design.

Knowledge of ETL/ELT development, orchestration, automation, and integration patterns.

Understanding of data governance, metadata management, data quality, security, privacy, and compliance practices.

Familiarity with cloud data platforms such as Snowflake, Databricks, Microsoft Fabric, or equivalent technologies.

Knowledge of BI, analytics, AI, and data science consumption patterns.

Strong analytical, problem-solving, and troubleshooting skills.

Ability to translate business needs into scalable and reusable data solutions.

Strong communication, collaboration, and technical documentation skills.

Ability to work effectively in a multidisciplinary and matrixed delivery environment.

How Data products deliver trusted, accessible, and reusable data assets that solve business problems and create measurable value.

Data products are scalable, performant, governed, and well-documented.

Data quality controls, validation processes, and monitoring capabilities are embedded throughout the solution lifecycle.

Deployments and changes are delivered through secure, consistent, and automated processes.

Data products support reporting, analytics, operational processes, AI, and advanced analytical use cases.

Production environments, business rules, metadata, lineage, and data assets are well-managed and traceable.

Operational issues are addressed quickly through robust support and continuous improvement practices.

Data products meet enterprise governance, security, privacy, and compliance requirements.

Reusable design patterns and automation improve delivery speed, consistency, and maintainability.

Data products continue to evolve and improve as business needs, technologies, and data sources change.