Data Engineer

Responsibilities
Architecture
Architect modern data platforms using Databricks and related cloud technologies, with experience in Microsoft Fabric, Snowflake, Azure, or similar platforms considered highly valuable.
Define scalable, secure, and maintainable data architectures that support analytics, reporting, machine learning, and AI initiatives
Lead infrastructure design, CI/CD implementation, Infrastructure-as-Code, and deployment automation
Evaluate architectural tradeoffs and recommend solutions aligned to client goals and long-term scalability
Establish engineering standards, best practices, and reusable design patterns across projects
Data Engineering & Development
Design and build enterprise-grade ETL/ELT pipelines and data integration solutions
Develop solutions utilizing Databricks, Microsoft Fabric, Azure Data Factory, Snowflake, DBT, Spark, PySpark, Python, and SQL
Optimize batch, streaming, and near-real-time data processing workloads
Implement data quality, lineage, governance, observability, and monitoring frameworks
Design semantic models and curated data products for BI, analytics, and AI consumption
Support implementation of modern lakehouse and data warehouse architectures
Project Planning & Delivery
Break down client requirements into actionable engineering workstreams
Contribute to pre-sales scoping and estimation, ensuring effort estimates are technically defensible before commitments are made
Drive backlog creation, refinement, estimation, and sprint planning activities
Identify technical risks, dependencies, and mitigation strategies
Ensure engineering work is delivered with high quality and within project budgets
Partner with project leadership to maintain project health and successful client outcomes
Client Engagement & Consulting
Lead technical discovery workshops, architecture reviews, and solution design sessions
Present recommendations to both technical and business stakeholders
Translate complex technical concepts into business-focused outcomes
Contribute to project estimation, solution design, and Statement of Work development
Identify opportunities for future phases and expanded client value
What We're Looking For
Required Qualifications
Strong experience with Microsoft Fabric, Databricks, Snowflake, and modern cloud data platforms
Advanced proficiency in SQL and Python
Experience building scalable ETL/ELT pipelines and enterprise data architectures
Strong understanding of dimensional modeling, lakehouse architectures, and analytics engineering
Experience with CI/CD, Infrastructure-as-Code, and cloud-native development practices
Familiarity with data governance, lineage, metadata management, and security best practices
Strong client-facing consulting and communication skills
Experience working in Agile delivery environments
Preferred Qualifications
Experience with Microsoft Purview, Unity Catalog, Snowflake Horizon, or similar governance platforms
Experience with Azure cloud architecture, networking, and security, including VNets, private networking, hybrid connectivity, and enterprise-scale data platform deployments.
Experience supporting machine learning, AI, or advanced analytics initiatives
Familiarity with Retrieval-Augmented Generation (RAG), vector databases, and AI-ready data architectures
Experience using AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, Windsurf, or similar technologies
Experience managing or formally leading engineering teams
Microsoft, Databricks, Snowflake, or cloud platform certifications

Work arrangement
No