Lead Azure Data Engineer

Das ist der Job

Lead end-to-end analytics projects — from requirement gathering to deployment.

Darum lohnt es sich

Design and implement scalable data models, ETL/ELT pipelines, and analytics layers using modern cloud ecosystems. Mentor junior team members, perform code reviews, and ensure adherence to quality standards. Work closely with cross-functional teams (engineering, product, business) to align data strategy with business goals.

Drive adoption of AI-assisted data engineering across the team: copilots for SQL/transformation/pipeline code, plus automated testing and documentation. Advanced SQL skills and a solid understanding of modern data architecture, including Data Lakehouse concepts, medallion architecture, star/snowflake schemas, and dimensional modeling.

Responsibilities Understand client requirements and lead the creation of robust data platforms leveraging Data Lakehouse architectures, serving as the primary subject matter expert for the Azure data ecosystem (Microsoft Fabric, Azure SQL, and Power BI).

Architect Microsoft Fabric reporting solutions, establishing medallion layering, managing capacity planning, ensuring freshness governance, and configuring CDC/mirroring from Azure SQL into Fabric.

Enforce strict data-layer segregation ensuring PII is excluded by construction, maintaining multi-tenant isolation within the analytics layer, and strictly adhering to 10+ year regulated data retention policies.

Lead complex migration efforts off legacy systems (e.g., AirTable, Smartsheet) to Microsoft Fabric, effectively managing mid-program form and rule drift, and establishing reliable, per-client export channels.

Develop and optimize complex SQL queries, Power BI semantic models, dashboards, and reports (transitioning from legacy LookML/BigQuery paradigms). Troubleshoot performance bottlenecks, data inconsistencies, and integration challenges. Stay up to date with advancements in the analytics and cloud data ecosystem.

Drive innovation in reporting and analytics capabilities. Recommend and implement new tools, frameworks, and practices to enhance the data platform. Define best practices, coding standards, and governance for analytics projects. Monitor data systems performance and implement optimization strategies.

Ensure data accuracy, integrity, privacy, security, and compliance through quality control procedures. Build and oversee AI-ready data pipelines, focused on feeding scrubbed, segmented data to assistive AI and integrating GenAI into analytics and data products. Define governance and quality standards for data consumed by LLMs and agents.

Requirements Demonstrated expertise with a minimum of 7 - 10 years of relevant experience in data engineering and business intelligence domain.

Strong primary expertise in the Azure Data Stack, specifically Azure SQL, Microsoft Fabric, and Power BI (semantic layer design, robust dashboard development), with a minimum of two years of hands‑on experience in these specific tools. Proficiency in Python for data engineering, ETL/ELT workflows, and automation tasks.

Experience designing and maintaining enterprise‑grade data pipelines — specifically in Azure environments. Proven track record of leading analytics projects and delivering scalable data solutions. Ability to function as a player‑coach — leading while also contributing hands‑on.

Solid understanding of data governance, security (especially around PII and multi‑tenant architectures), and performance optimization practices. Strong communication skills and ability to translate complex technical concepts for business stakeholders. #J-18808-Ljbffr