Note: The job is a remote job and is open to candidates in USA. The hiring client is an unnamed Appleton-based software company specializing in tax compliance technology. The Senior Data Engineer will build its data infrastructure and enterprise data and analytics practice, supporting business intelligence, advanced analytics, and emerging AI initiatives while helping shape technical direction.

Responsibilities

• Design, develop, and maintain scalable data pipelines and infrastructure using Snowflake, Microsoft Azure, and modern data engineering tools
• Own end-to-end data engineering initiatives, including requirements gathering, architecture, ingestion, transformation, modeling, governance, and delivery
• Architect data integration solutions across operational databases, SaaS applications, external sources, and enterprise systems using batch, streaming, and change data capture (CDC) patterns
• Build and maintain transformation frameworks using dbt or comparable technologies, establishing standards for data quality, testing, documentation, and maintainability
• Develop and optimize SQL queries, data models, and Snowflake workloads for performance, scalability, and cost efficiency
• Establish data engineering best practices around CI/CD, version control, orchestration, observability, pipeline reliability, and incident response
• Support data governance initiatives, including metadata management, data lineage, schema management, security, and regulatory compliance
• Partner with business stakeholders to translate requirements into technical solutions, supporting reporting, analytics, Power BI, and downstream applications
• Help build the data foundation for emerging AI capabilities, including AI-assisted analytics, regulatory intelligence, and agent-based workflows
• Collaborate with leadership on technical strategy, platform improvements, and the development of a scalable data and analytics practice
• Mentor fellow engineers and contribute to the team's technical standards and capabilities

Skills

• NO C2C | USC or GC Holder only
• 4–7+ years of professional data engineering experience, including hands-on ownership of production data platforms and complex data pipelines
• Strong Snowflake experience, including data warehouse design, performance tuning, access controls, and cost optimization
• Strong experience with Microsoft Azure data services, including Azure Data Factory, Azure SQL, Azure Storage, and related cloud infrastructure
• Advanced SQL skills, including complex queries, performance optimization, data modeling, and schema design
• Experience with modern data transformation tools such as dbt, Dataform, or equivalent frameworks
• Experience with workflow orchestration tools such as Azure Data Factory, Airflow, or Dagster
• Proficiency in Python, Scala, or another programming language used for data engineering and integration
• Strong understanding of ETL/ELT, data ingestion, dimensional modeling, data warehousing, and modern data architecture
• Experience implementing CI/CD, automated testing, data quality monitoring, and production pipeline support
• Working knowledge of data governance, lineage, metadata management, and security practices
• Experience using AI-assisted development tools such as GitHub Copilot, Claude, or comparable technologies
• Strong communication skills and demonstrated ability to work directly with business stakeholders, translate requirements, and independently drive projects through completion
• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related discipline, or equivalent experience
• Snowflake or Microsoft Azure certifications
• Experience with Power BI, including semantic models, datasets, and reporting integrations
• Experience with AI/ML integrations, Azure OpenAI, Azure AI Foundry, or AI agents
• Familiarity with streaming technologies such as Kafka or Azure Event Hubs
• Experience with infrastructure-as-code tools such as Terraform or Bicep
• Exposure to regulated environments, SOC 2 compliance, or sensitive-data governance
• Experience establishing data engineering standards or contributing to a Data & Analytics Center of Excellence

Benefits

• Full-time remote work; onsite, hybrid, and remote candidates will be considered.

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