Principal Data Engineer

As a member of the Data Hub leadership team, you will contribute to technology strategy, roadmap prioritization, architecture governance, vendor evaluation, and executive reporting while remaining deeply engaged in day-to-day engineering activities.

Main duties and responsibilities

Infrastructure and Platform Engineering

• Design, build, and maintain scalable, secure, and reliable cloud-native data platform infrastructure.
• Develop infrastructure-as-code, CI/CD pipelines, deployment automation, and environment management processes.
• Partner with Corporate IT, Security, and platform vendors to ensure compliance, reliability, and operational excellence.
• Improve platform observability through monitoring, alerting, logging, and performance tracking.

Data Architecture and Modelling

• Design scalable data models supporting analytics, reporting, AI/ML, and operational use cases.
• Define and evolve data architecture standards, patterns, and best practices across the Data Hub ecosystem.
• Ensure data solutions align with governance, lineage, security, and regulatory requirements.
• Guide engineering teams in implementing maintainable and extensible data structures.

Data Engineering and Solution Delivery

• Build and optimize data ingestion, transformation, and delivery pipelines across multiple business domains.
• Lead technical design reviews and contribute directly to implementation of complex data engineering initiatives.
• Collaborate with Product, Analytics, AI/ML, and business stakeholders to translate requirements into scalable technical solutions.
• Provide hands-on support for critical platform initiatives, migrations, and modernization programs.

DevOps and Engineering Excellence

• Establish and enforce software engineering, GitOps, DevOps, testing, and deployment standards.
• Drive automation across development, deployment, monitoring, and operational processes.
• Promote best practices for code quality, documentation, technical debt management, and release management.
• Conduct architecture reviews and code reviews to ensure consistency and maintainability.

Quality, Performance, and Reliability Optimization

• Identify and resolve performance bottlenecks across pipelines, databases, and infrastructure.
• Define and implement data quality frameworks, automated validation processes, and operational controls.
• Optimize platform cost, scalability, reliability, and processing efficiency.
• Lead root-cause analysis and remediation efforts for production incidents and operational challenges.

Technical Leadership and Mentorship

• Mentor Data Engineers, Analytics Engineers, and other technical contributors through coaching, code reviews, pair programming, and design guidance.
• Act as a trusted technical advisor across multiple teams and business units.
• Share knowledge, develop engineering standards, and promote continuous improvement across the Data Hub organization.
• Help elevate the technical capabilities of the broader engineering team.

Strategic Planning and Cross-Functional Collaboration

• Participate in Data Hub leadership activities, including roadmap planning, prioritization discussions, technology evaluations, and executive reporting.
• Provide technical recommendations on platform investments, vendor selection, architecture direction, and engineering standards.
• Collaborate closely with Data Product Management, AI/ML teams, business stakeholders, and Corporate IT to ensure alignment between business priorities and technical execution.

Education and Experience

Education: Bachelor’s degree in computer science, engineering, Information Systems, or related field. Equivalent experience considered. Advance degree preferred but not required.

Required:

• 8+ years of experience in Data Engineering, Platform Engineering, Infrastructure Engineering, or related technical disciplines.
• Demonstrated expertise designing and implementing modern cloud-based data platforms.
• Strong hands-on experience with data warehousing, orchestration, transformation, and DevOps technologies.
• Experience building and optimizing large-scale data pipelines and data models.
• Deep understanding of software engineering principles, infrastructure automation, and production operations.
• Experience operating in highly regulated, compliance-sensitive, or enterprise environments.
• Proven track record of leading technical initiatives through influence rather than direct authority.

Preferred:

• Experience within healthcare, life sciences, pharmaceutical, or analytics organizations.
• Experience supporting AI/ML platforms, MLOps capabilities, feature stores, or model deployment frameworks.
• Familiarity with claims, formulary, commercial, or real-world evidence datasets.
• Experience leading enterprise-scale data migrations, modernization programs, or platform consolidations.
• Exposure to global engineering teams and distributed delivery models.

Knowledge, Skills and Competencies

Technical Expertise

• Advanced knowledge of cloud data platforms, modern data architecture, and platform engineering.
• Deep expertise in data modelling, query optimization, and analytical data design.
• Ability to evaluate and select appropriate technologies, frameworks, and architectural approaches.
• Expert-level SQL and strong Python skills.

Infrastructure and DevOps

• Strong understanding of CI/CD, GitOps, Infrastructure-as-Code, containerization, and deployment automation.
• Experience implementing operational monitoring, incident management, alerting, and observability solutions.
• Knowledge of security, access management, and platform governance best practices.
• Strong understanding of scalability, resilience, and performance engineering.

Leadership and Influence

• Leads through credibility, expertise, and collaboration rather than formal authority. Strong mentoring and coaching capabilities.
• Comfortable facilitating technical discussions and driving consensus among diverse stakeholders and ability to influence architecture and technology decisions across teams.

Communication and Collaboration

• Excellent written and verbal communication skills and able to communicate complex technical concepts to both technical and non-technical audiences.
• Effective stakeholder management across engineering, product, business, and executive teams. Strong decision-making and prioritization skills in ambiguous environments.

Technical Environment

You will work across a modern data and AI ecosystem that may include:

• Data Platforms: Snowflake, AWS Redshift, S3
• Transformation: dbt, Matillion
• Orchestration: Airflow, Dagster
• Data Governance: Snowflake Horizon
• Data Quality: dbt tests, Elementary
• Infrastructure & DevOps: Terraform, GitHub Actions, Azure DevOps
• AI/ML: Snowpark ML, MLflow, Snowflake Feature Store, LangChain
• Languages: SQL, Python

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