Note: The job is a remote job and is open to candidates in USA. Protégé Hunters has an immediate Direct hire opportunity for a Senior Data Engineer to design, build, and optimize enterprise data platforms. The role involves leading the development of modern lakehouse and data warehouse architectures, ensuring data governance, and collaborating with business users to translate requirements into actionable data solutions.
Responsibilities
- Design, build, and scale end-to-end ETL/ELT pipelines and modern lakehouse architectures using Databricks (Delta Lake, Unity Catalog) and Microsoft Fabric (OneLake, Data Factory)
- Architect robust, reusable data models using medallion structures and implement enterprise semantic layers optimized for Power BI
- Build and maintain Power BI datasets, semantic models, and shared data sources that support: Operational reporting and Financial and performance reporting
- Ensure reports are sourced from governed, consistent datasets, reducing duplication and conflicting metrics
- Partner directly with business users and analysts to understand reporting requirements and translate them into data models and reusable datasets
- Troubleshoot production environments, optimize Spark and PySpark workloads, and fine-tune data workflows for maximum scalability, reliability, and cost-efficiency
- Enforce best practices for data governance, access controls, and data quality assurance
- Define a practical, phased enterprise data strategy and roadmap aligned to business priorities
- Establish architectural standards that balance scalability, cost, and usability
- Translate business and operational needs into actionable data solutions—not just diagrams
- Personally implement data governance practices, including: Data ownership and stewardship models, Data quality rules and monitoring, Metadata, documentation, and lineage standards
- Ensure data platforms meet security, access control, and compliance requirements in coordination with IT security
- Partner closely with IT, Finance, Operations, and business leaders to ensure data solutions are usable and trusted
- Act as the technical authority on enterprise data decisions
Skills
- Experience: 7+ years of professional experience in data engineering, platform development, or analytics engineering
- Experience integrating data across multiple SaaS/vendor platforms
- Proven ability to design and build cloud-native data architectures
- Solid understanding of data governance implemented in real environments (not just frameworks)
- SME with Design and personally implement core components of the enterprise data architecture using Microsoft Fabric (Lakehouse, Warehouse, OneLake) and Azure data services
- Strong SQL skills, including data modeling, transformations, query optimization, and troubleshooting
- Advanced, production-level proficiency in Databricks, Apache Spark, and PySpark
- Hands-on implementation experience with Microsoft Fabric (Data Factory, Lakehouse, Warehousing storage, and modern data integration tools
- Experience integrating ERP, SaaS, APIs, and operational systems into a centralized data platform
- Experience working with structured and semi-structured data sources, including REST APIs, JSON, CSV, Excel, databases, and SaaS exports
- Familiarity with data transformation, orchestration, and integration tools such as dbt, Airbyte, Fivetran, Dagster, Airflow, GitHub Actions, or similar platforms
- Experience with Git-based development workflows and basic software engineering practices
- Interest in AI-enabled analytics, automation, and internal tools, including practical use of AI coding assistants and LLM-based workflows
- Experience with business intelligence and reporting tools, especially Power BI, including data modeling, Power Query, DAX, dashboard development, and report performance tuning
- Certifications: Microsoft Certified: Fabric Data Engineer Associate (DP-700) or Azure Databricks Data Engineer Associate
- AI & ML: Experience supporting generative AI, LLMs, or machine learning pipelines within a data estate
- Bachelor's degree in computer science, Data Engineering, Business Administration, or a related field
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