Informacje o stanowisku

For our client we are looking for a person for the position of: Solutions Architect

What we expect

- 8+ years of experience in data engineering or data architecture roles with strong exposure to AWS services

- Background in architecting and leading self-service data platform capabilities

- Deep understanding of Data Mesh, data lakehouse architecture

- Hands-on experience with data services such as AWS Glue, Amazon Redshift, as well as proficiency in Python, SQL, and dbt

- Hands-on experience with CI/CD, infrastructure as code, and modern DevOps practices

- Experience leading teams or mentoring junior engineers.

- Excellent communication and leadership skills with the ability to influence both technical and non-technical stakeholders

Preferred Qualifications:

- Experience with data governance frameworks, lineage, cataloging, and metadata management

- Familiarity with data privacy, compliance, and security best practices.

Prior experience working in a highly collaborative environment with product, data science, and analytics teams

Employment agency entry number 47

this job offer is intended for people over 18 years of age

What we offer

• Possibility of hybrid/remote work
• Stable employment based on a B2B contract
• Working in an international environment
• Flexible working hours

Your tasks

- Start the day with a cross-functional stand-up to sync with engineers and product managers.

- Review architectural proposals, provide technical guidance to engineers, and troubleshoot design issues.

- Collaborate with stakeholders on new data initiatives—reviewing use cases, proposing scalable design patterns, and aligning on implementation plans.

- Participate in code reviews, data model reviews, and peer design discussions to ensure alignment with platform strategy.

- Draft and review architectural diagrams and design documents, platform roadmaps, and reusable design artifacts.

- Meet with enterprise security or governance teams to ensure compliance and data integrity across domains.

- Evaluate or implement tools like monitoring frameworks, observability, or data quality checks.

- Engage in platform optimization efforts (performance tuning, cost management, etc.)

- Stay updated with the latest in the data ecosystem and assess feasibility of new tools or approaches.

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