Principal Data Engineer

The Opportunity

We are looking for a high-caliber Principal Data Engineer to become Humaneva’s leading technical authority for the data ecosystem. This is a predominantly hands-on individual contributor role for an engineer who can lead the modernization of our data warehouse, improve pipeline reliability, and shape the future data platform through working solutions.

You will have substantial autonomy over data architecture, engineering standards, and technology selection. You will assess whether Snowflake should remain on the primary platform or whether Humaneva should evolve toward a more open, heterogeneous, or domain-oriented model. Significant decisions must be supported by clear context, alternatives, trade-offs, and recommendations that the CTO and technology teams can understand and challenge.

ROLE MISSION

Establish reliable, scalable, secure, and governed data engineering at Humaneva

Key Responsibilities

• Hands-on Data Engineering: Design, build, test, deploy, and operate production-grade pipelines, transformations, integrations, data models, and reusable platform capabilities. Personally lead the most complex topics and resolve critical engineering problems.
• Data Platform Architecture: Assess the current DWH and define the target architecture. Decide which components should be retained, modernized, replaced, or redesigned, including the future role of Snowflake and the suitability of centralized, heterogeneous, or data mesh approaches.
• Reliability, Quality & Observability: Introduce measurable reliability standards, automated data-quality controls, end-to-end monitoring, logging, alerting, incident handling, and root-cause analysis. Eliminate silent failures and reduce dependency on manual checks.
• Technology Strategy: Independently evaluate and select technologies based on business fit, reliability, interoperability, security, maintainability, talent availability, and total cost of ownership. Use proofs of concept where evidence is needed.
• Self-Service Enablement: Enable governed by self-service analytics through Power BI, SAS, and other approved platforms. Provide trusted and well-documented data products without weakening security, quality, or ownership controls.
• Technical Leadership: Lead through delivery, design reviews, code reviews, and practical problem-solving. Mentor less-experienced engineers while raising implementation, testing, documentation, and operational standards across the team.
• Stakeholder Collaboration: Work closely with Product, Clinical Operations, Finance, and other functions. Translate business needs into robust technical designs and explain significant decisions in clear business language.
• Cost & Performance: Optimize compute, storage, processing, and operational costs. Introduce visibility into workload behavior and cost drivers and evaluate alternatives using total cost of ownership.

Scope Across the Data Lifecycle

• Source-system integration and batch or near-real-time ingestion
• Data transformation, orchestration, modeling, and serving
• Data warehouse and broader data platform architecture
• Metadata, cataloguing, lineage, and data-product ownership
• Data quality, automated testing, observability, and alerting
• CI/CD, infrastructure automation, release controls, and operational practices
• Identity, access management, privacy, retention, and auditability
• Interfaces for Power BI, SAS, applications, analytics, and future AI workloads

Security, Governance & Compliance

• Embed privacy, security, and compliance requirements into architecture and engineering practices.
• Implement appropriate access controls, segregation of duties, traceability, lineage, and audit evidence.
• Support GDPR requirements and regulated use cases, including applicable GxP and 21 CFR Part 11 expectations.
• Work with Quality, Security, Legal, and business owners to translate controls into practical engineering solutions.

Decision Authority

The Principal Data Engineer is empowered to make independent technical decisions concerning data architecture, platform design, technology selection, engineering standards, reliability controls, observability, CI/CD, release practices, performance, and cost optimization.

For material decisions, the role will document the context, constraints, alternatives considered, expected benefits, risks, trade-offs, cost implications, and recommended direction.

Technical Requirements

• Engineering Depth: Extensive experience designing, delivering, and operating production-grade data platforms, with strong hands-on expertise in data engineering and software engineering.
• Modernization Experience: Proven experience modernizing data warehouses or building new data platforms and migration paths.
• Data Engineering: Advanced SQL, strong data-modeling skills, and strong programming capability in at least one language used for production of data engineering.
• Reliability Engineering: Practical experience with automated testing, data-quality controls, monitoring, alerting, incident diagnosis, and root-cause analysis.
• Cloud & Delivery: Strong understanding of cloud data architecture, version control, CI/CD, infrastructure automation, and controlled deployment practices.
• Architecture Judgment: Ability to evaluate vendor-specific and open technologies without being constrained by a predefined stack.
• Leadership & Communication: Ability to lead complex work through implementation, mentoring engineers, and communicate technical decisions to engineering, executive, and business audiences.

Preferred Qualifications

• Experience with Snowflake architecture, engineering, performance optimization, and cost management.
• Experience with AWS, Azure, or multi-cloud environments.
• Experience enabling Power BI, SAS, or comparable self-service analytics platforms.
• Experience with data mesh, domain-oriented data products, or heterogeneous platform architectures.
• Experience in clinical research, healthcare, or another regulated industry.
• Familiarity with GDPR, GxP, audit-trail requirements, or 21 CFR Part 11.
• Experience with Platform Engineering, Infrastructure as Code, or shared developer capabilities.
• Experience preparing reliable data foundations for machine learning or generative AI.

First-Year Measures of Success

• Complete a documented assessment of the existing DWH, pipelines, platform risks, and modernization priorities.
• Establish an agreed target architecture and roadmap, including a clear recommendation on Snowflake’s future role.
• Implement meaningful observability and automated quality controls for critical data flows.
• Reduce the frequency and impact of pipeline failures and eliminate critical silent failures.
• Define ownership and measurable reliability expectations for critical data products.
• Introduce consistent engineering, testing, deployment, and documentation standards adopted by the team.
• Improve the team’s technical capability through hands-on delivery and practical mentoring.
• Provide reliable, governed data access for Product, Clinical Operations, Finance, and other functions.

Future Growth

The primary responsibility is Data Engineering. The role offers a credible path toward broader responsibility, either in management as Head of Data or in an expert technical track. Depending on organizational needs and demonstrated capability, the scope may later expand into Platform Engineering and AI enablement. These areas are potential directions, not guaranteed responsibilities at the start of the role.

Why Join Us?

This is an opportunity to shape the future of data engineering at Humaneva rather than maintaining an existing environment. You will have the autonomy to challenge the current architecture, choose appropriate technologies, and build dependable foundations for analytics, operations, and future AI capabilities.

The role combines deep hands-on engineering with organization-wide technical influence and a potential path toward Managersial roles and expertise in Platform engineering and AI.