Principal Data Engineer

Job not on LinkedIn

🔥 0 minutes ago

🇵🇱 Poland – Remote

⏰ Full Time

🔴 Lead

🚰 Data Engineer

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Humaneva Group

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

🏥 Healthcare

💊 Pharmaceuticals

🤝 B2B

💰 $50M Private Equity Round - Humaneva on 2024-09

Healthcare • Pharmaceuticals • B2B

Humaneva Group is a clinical-research-focused organization that integrates patient care, scientific innovation, and data-driven approaches to provide reliable clinical research data to the medical community. The Group comprises three interrelated companies: a research site network, a contract research organization (CRO), and a technology platform that together create a connected data stream between patients, investigators, research centers, and data standards. Humaneva emphasizes integration of clinical operations with technology and appears to support therapeutic areas such as oncology, according to its site navigation.

📋 Description

• Design, build, test, deploy, and operate production-grade pipelines, transformations, integrations, data models, and reusable platform capabilities • Lead complex data-engineering topics and resolve critical engineering problems • Assess the current data warehouse and define the target architecture • Decide which platform components should be retained, modernized, replaced, or redesigned, including Snowflake’s future role • Introduce measurable reliability standards, automated data-quality controls, end-to-end monitoring, logging, alerting, incident handling, and root-cause analysis • 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 • Enable governed self-service analytics through Power BI, SAS, and other approved platforms • Provide trusted and well-documented data products while maintaining security, quality, and ownership controls • Lead through delivery, design reviews, code reviews, and practical problem-solving • Mentor less-experienced engineers and raise implementation, testing, documentation, and operational standards • Collaborate with Product, Clinical Operations, Finance, Quality, Security, Legal, and business owners • Translate business needs into robust technical designs and explain significant decisions in clear business language • Optimize compute, storage, processing, and operational costs • Introduce visibility into workload behavior and cost drivers and evaluate alternatives using total cost of ownership • Establish source-system integration and batch or near-real-time ingestion • Manage data transformation, orchestration, modeling, and serving • Address metadata, cataloguing, lineage, data-product ownership, CI/CD, infrastructure automation, release controls, identity, access management, privacy, retention, auditability, and interfaces for analytics and future AI workloads • Embed privacy, security, and compliance requirements into architecture and engineering practices • Implement 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 • Document context, constraints, alternatives, expected benefits, risks, trade-offs, cost implications, and recommended direction for material decisions • Assess the existing DWH, pipelines, platform risks, and modernization priorities during the first year • Establish a target architecture and roadmap, including a recommendation on Snowflake’s future role • Implement observability and automated quality controls for critical data flows • Reduce 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 • Improve team technical capability through hands-on delivery and practical mentoring • Provide reliable, governed data access for Product, Clinical Operations, Finance, and other functions

🎯 Requirements

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

🏖️ Benefits

• Employee position • Predominantly hands-on individual contributor role with substantial autonomy over data architecture, engineering standards, and technology selection • Potential path toward broader responsibility as Head of Data or an expert technical track • Potential future expansion into Platform Engineering and AI enablement