Principal Data Engineer

This is a fully remote position, open to applicants in Poland.

📋 Description

• Design, develop, test, deploy, and manage production-level pipelines, transformations, integrations, data models, and reusable platform capabilities.

• Lead intricate data engineering initiatives and resolve critical engineering challenges.

• Evaluate the existing data warehouse and establish the target architecture.

• Determine which platform components should be preserved, modernized, replaced, or redesigned, including the prospective role of Snowflake.

• Introduce quantifiable reliability standards, automated data quality controls, comprehensive monitoring, logging, alerting, incident management, and root cause analysis.

• Assess and select technologies based on alignment with business needs, reliability, interoperability, security, maintainability, talent availability, and total cost of ownership.

• Utilize proofs of concept when evidence is required.

• Facilitate governed self-service analytics through Power BI, SAS, and other authorized platforms.

• Deliver trusted and well-documented data products while upholding security, quality, and ownership controls.

• Guide the process through delivery, design reviews, code reviews, and practical problem-solving.

• Mentor junior engineers and enhance standards for implementation, testing, documentation, and operations.

• Collaborate with Product, Clinical Operations, Finance, Quality, Security, Legal, and business stakeholders.

• Convert business requirements into robust technical designs and articulate significant decisions in a clear business context.

• Optimize computing, storage, processing, and operational expenditures.

• Provide insights into workload behavior and cost drivers while assessing alternatives using total cost of ownership.

• Establish source-system integration and manage batch or near-real-time data ingestion.

• Oversee data transformation, orchestration, modeling, and serving processes.

• Address metadata, cataloging, lineage, data product ownership, CI/CD, infrastructure automation, release controls, identity and access management, privacy, retention, auditability, and analytics interfaces for future AI workloads.

• Integrate privacy, security, and compliance requirements into architectural and engineering practices.

• Implement access controls, segregation of duties, traceability, lineage, and audit evidence.

• Support GDPR requirements and regulated use cases, including relevant GxP and 21 CFR Part 11 expectations.

• Document context, constraints, alternatives, expected benefits, risks, trade-offs, cost implications, and recommended directions for significant decisions.

• Evaluate the current DWH, pipelines, platform risks, and modernization priorities during the initial year.

• Develop a target architecture and roadmap, including recommendations for Snowflake’s future role.

• Implement observability and automated quality controls for essential data flows.

• Minimize pipeline failures and eradicate critical silent failures.

• Define ownership and measurable reliability expectations for essential data products.

• Establish consistent engineering, testing, deployment, and documentation standards.

• Enhance team technical capabilities through hands-on delivery and practical mentoring.

• Provide dependable, governed data access for Product, Clinical Operations, Finance, and other departments.

⛳️ Requirements

• Extensive experience in designing, delivering, and operating production-grade data platforms.

• Strong hands-on proficiency in data engineering and software engineering.

• Proven track record in modernizing data warehouses or creating new data platforms and migration pathways.

• Advanced SQL skills.

• Strong data modeling expertise.

• Proficient programming skills in at least one language utilized for production data engineering.

• Practical experience with automated testing, data quality controls, monitoring, alerting, incident diagnostics, and root cause analysis.

• Comprehensive understanding of cloud data architecture, version control, CI/CD, infrastructure automation, and controlled deployment practices.

• Capability to assess vendor-specific and open technologies without being limited by a predefined stack.

• Experience leading complex projects through implementation and mentoring engineers.

• Ability to convey technical decisions to engineering, executive, and business audiences.

• Experience with Snowflake architecture, engineering, performance optimization, and cost management.

• Familiarity with AWS, Azure, or multi-cloud environments.

• Experience enabling Power BI, SAS, or similar self-service analytics platforms.

• Background in data mesh, domain-oriented data products, or heterogeneous platform architectures.

• Experience in clinical research, healthcare, or other regulated industries.

• Knowledge of GDPR, GxP, audit trail requirements, or 21 CFR Part 11.

• Experience in Platform Engineering, Infrastructure as Code, or shared developer capabilities.

• Preparedness in establishing reliable data foundations for machine learning or generative AI.

🏝️ Benefits

• Employee position.

• Primarily a hands-on individual contributor role with significant autonomy over data architecture, engineering standards, and technology selection.

• Potential pathway toward increased responsibilities as Head of Data or an expert technical track.

• Possible future expansion into Platform Engineering and AI enablement.