Mid-Level Data Engineer
Kyriba
AWSETLPythonPyTorchScalaScikit-LearnSQLTensorflow
Data Architect / Platform Specialist – Enterprise, Databricks
Kyriba
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇵🇱 Poland
Visit company websiteJob Level
Mid-LevelSenior
Tech Stack
AWSCloudETL
About the role
- Design, implement, and evolve enterprise data architectures spanning multiple business domains and use cases.
- Define and enforce architectural standards and best practices for data modeling, integration, and governance.
- Ensure data solutions are scalable, secure, and optimized for reporting, BI, advanced analytics, ML, and GenAI workloads.
- Lead Databricks platform implementation and apply Databricks data design patterns, including Delta Lake architecture and unified analytics.
- Architect Databricks environments to support batch, streaming, real-time, and advanced analytics; integrate with AWS S3 and enterprise platforms.
- Act as primary interface between data, IT, business, and analytics teams; drive data standardization across finance, operations, HR, supply chain, and customer domains.
- Architect and optimize data flows for operational and analytical reporting, BI dashboards (e.g., QlikView), and self-service analytics.
- Partner with Data Scientists and ML Engineers to ensure ML/GenAI readiness (feature stores, model training, scalable inference).
- Implement enterprise data governance, data quality, security, compliance frameworks; oversee metadata management, lineage, and cataloging.
- Evaluate and adopt emerging technologies; foster continuous improvement and best practices in data architecture and platform engineering.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or related field.
- Extensive experience as a Data Architect or Platform Specialist supporting multiple business domains across large organizations.
- Proven expertise in designing and implementing data architectures on Databricks and AWS S3.
- Deep knowledge of data modeling, data warehousing, ETL/ELT, and cloud data platforms.
- Experience with Databricks best practices for reporting, BI, ML, and GenAI.
- Strong understanding of BI tools (e.g., QlikView) and their integration with enterprise data platforms.
- Familiarity with ML/GenAI architectures, workflows, and operationalization.
- Comprehensive knowledge of data governance, security, and compliance frameworks.
- Outstanding communication, leadership, and stakeholder management skills.
- Nice to have: Certifications in Databricks, AWS, or enterprise architecture frameworks (e.g., TOGAF).
- Nice to have: Experience with data mesh, data fabric, or modern data stack concepts.
- Nice to have: Exposure to automation and integration platforms (e.g., MuleSoft).
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
data architecturedata modelingETLELTdata warehousingDatabricksAWS S3BIMLGenAI
Soft skills
communicationleadershipstakeholder management
Certifications
Databricks certificationAWS certificationTOGAF certification