Data Engineer

The Data Engineer role at Dolese involves designing, building, and scaling data and AI foundations to power enterprise reporting, analytics, and automation. The successful candidate will have strong proficiency in SQL and Python, experience with data warehousing and lakehouse architectures, and a strong understanding of software engineering best practices.

Requirements

• Design, build, and maintain scalable, reliable data pipelines (ETL/ELT) that support analytics, reporting, and machine learning workloads.
• Develop and support machine learning and large language model (LLM)–enabled capabilities to automate reporting, insight generation, and natural language data access.
• Enable AI-driven self-service analytics, including natural language query and AI-assisted reporting tools, allowing business users to interact directly with governed data assets.
• Ensure data quality, performance, observability, and reliability across production data and AI systems.
• Support operational readiness of data pipelines and AI services, including monitoring, issue resolution, and continuous improvement.
• Design and maintain curated data models, feature datasets, and analytical layers that enable BI, advanced analytics, and ML/AI use cases.
• Support enterprise data governance by ensuring accuracy, consistency, and quality across analytics assets.
• Collaborate with analytics, BI, and platform teams to improve data pipelines, definitions, and reliability and support scalable modern data architecture (e.g., lakehouse patterns).
• Enable BI and reporting teams by providing performant, well-modeled, and trusted datasets that serve as the foundation for dashboards and reports.
• Support integration of AI-powered capabilities within BI platforms (e.g., Copilot, intelligent alerting, embedded forecasting).
• Work closely with analytics, BI, and business stakeholders to understand reporting, automation, and AI requirements.
• Partner with business stakeholders to support KPI, metric, and measurement strategy enablement through strong data modeling and governance.
• Communicate data platform capabilities, limitations, and best practices to technical and non-technical partners.
• Provide guidance and knowledge sharing on data engineering, automation, AI/ML enablement, and platform best practices.
• Support continuous improvement of analytics processes, including automation, documentation, and standardization.
• Apply software engineering best practices, including version control, code reviews, testing, and CI/CD where applicable.

Benefits

• Profit sharing
• Strong employee benefits
• Competitive salary
• Opportunities for growth and development