Data Architect with AWS and Databricks in Piteşti

DataBrasov+8 more What's this role about? Lead and Oversee Data Architecture: Design, implement, and manage scalable, high-performance data architectures on AWS and Databricks platforms to support business needs. End-to-End Development Lifecycle: Participate in all phases of the application development lifecycle, from requirements gathering to deployment and maintenance. Technical Leadership: Provide technical guidance and mentorship to data engineers, ensuring best practices in coding, architecture, and overall data management. Solution Design: Collaborate with stakeholders to understand business requirements and translate them into effective, scalable data solutions. Participate in both logical and physical design phases. Complex Problem Solving: Address complex technical challenges and provide innovative solutions, ensuring robustness, scalability, and performance. Quality Assurance: Review code and enforce quality standards within the team, fostering a culture of continuous improvement. Agile Collaboration: Work as an integral part of a Scrum team, contributing to sprint planning, execution, and retrospectives. Cross-functional Collaboration: Engage in a broad range of complex technical or professional work activities, collaborating with cross-functional teams to ensure seamless data integration and management. Strategic Direction: Work under general direction within a clear framework of accountability, providing substantial personal responsibility and autonomy in strategic decisions.What skills and experience do you need? Extensive Data Engineering Experience: Proven experience as a Data Engineer in Databricks environments, with a deep understanding of Databricks data engineering tools (Unity Catalog, Delta Lake, Spark SQL, Spark Streaming, Delta Sharing). AWS Expertise: Extensive experience in configuring and optimizing AWS data services (S3, Lambda, Glue, Step Functions, DynamoDB, Kinesis, Athena, EventBridge, SNS) for seamless data integration and event-driven workflows. Advanced Data Pipeline Management: Expertise in developing and maintaining data pipelines using Python within an AWS-based platform, ensuring efficient data processing and management. Data Automation and Ingestion: Strong skills in data automation and ingestion, utilizing tools and frameworks to streamline data workflows. Infrastructure as Code (IaC): Proficiency in implementing IaC using AWS CDK to manage and automate cloud resources effectively. Apache Iceberg: Experience utilizing Apache Iceberg to enhance data storage and organization for improved scalability and performance. Version Control and CI/CD: Manage version control and CI/CD workflows using GitHub, leveraging GitHub Actions for continuous integration and deployment. Monitoring and Observability: Enhance monitoring and observability of data pipelines using Datadog, optimizing performance and reliability. Client-facing and Communication Skills: Excellent client-facing skills with the ability to communicate complex technical concepts to non-technical stakeholders. English Proficiency: Strong English language skills, both written and verbal. Agile Methodologies: Experience working within an Agile delivery framework, contributing to continuous improvement and efficient project delivery.