Senior Data Engineer - IGEN

Senior Data Engineer

The Senior Data Engineer is the technical leader for IGEN's Data Foundation — architecting, building, and operating the Snowflake data platform, Azure-native ingestion infrastructure, and governed data products that the broader organization depends on for trusted decision-making and AI-powered capability. Owns the architectural integrity of the platform — ingestion patterns, transformation frameworks, schema governance, lineage, and pipeline SLAs — and partners directly with the Director, Data & Analytics on platform strategy. Operates with AI-assisted development capabilities as a standard part of the discipline, builds within the broader Microsoft and Azure ecosystem, and mentors Data Engineers as the function scales. Works inside the AI governance framework developed in partnership with U.S. Venture Legal, InfoSec, Privacy, and Compliance, ensuring the data foundation supports IGEN's SOC 2 Type 2 obligations and the responsible deployment of AI capabilities across the platform.Preferred location for this position is our Corporate Headquarters in Appleton, WI, with hybrid work options available.

Job Responsibilities

• Leads the design and implementation of enterprise-grade data pipelines on Snowflake and Azure — owning ingestion, transformation, and serving layers for governed data products that support leadership reporting, client experience analytics, product usage analytics, and the regulatory intelligence pipeline.
• Architects ingestion and integration patterns across IGEN's source systems — including operational databases, SaaS platforms, regulatory data sources, and product telemetry — applying CDC, batch, and streaming patterns where appropriate.
• Establishes and maintains transformation frameworks using dbt as the modeling standard, defines layering and naming conventions, and ensures all pipelines are version-controlled, tested, and observable.
• Owns pipeline SLAs across the platform — defining and tracking freshness, completeness, and error-rate targets for every data product, with documented runbooks and on-call rotation for production-critical pipelines.
• Drives schema governance and data modeling discipline — including conceptual, logical, and physical data models, naming conventions, and the management of breaking changes across consuming Power BI dashboards and downstream AI capabilities.
• Implements and maintains data lineage, catalog, and metadata management — ensuring every governed KPI has a traceable source, definition, owner, and certification status.
• Partners with the Director, Data & Analytics on the Center of Excellence operating model — including intake processes, prioritization, stakeholder reviews, and the technical roadmap for the data platform.
• Uses AI development assistants — GitHub Copilot, Microsoft Copilot, Claude, and emerging agent-based tooling — as a standard part of daily engineering practice. Applies sound judgment in reviewing, validating, and securing AI-generated transformation logic and pipeline code.
• Designs and operates the regulatory intelligence pipeline — ingesting jurisdictional updates from federal, state, and local sources and structuring them for downstream AI-assisted change-impact analysis against ComplyIQ, Tax Determination, and Reconciliation modules.
• Implements data security, privacy, and compliance controls aligned with SOC 2 Type 2 obligations and the AI governance framework — including data classification, access controls, encryption at rest and in transit, PII handling, and audit trail requirements. Partners with U.S. Venture InfoSec, Privacy, and Compliance as required.
• Drives engineering best practices across the data function — including CI/CD for data, version control, automated testing of transformation logic, observability, and incident response.
• Mentors Data Engineers — through code review, architectural pairing, and knowledge sharing — and contributes to the growth of the team's technical capability as IGEN scales the function.
• Optimizes cost and performance across the Snowflake platform — including warehouse sizing, query optimization, storage management, and chargeback visibility for consuming functions.
• Continuously develops professionally — particularly in AI-assisted development, agentic data workflows, and emerging Microsoft and Azure data platform capabilities. Stays current as the boundary between human and agent work continues to evolve.
• Lives the company values of High Performance, Caring Relationships, Strategic Foresight, and Entrepreneurial Spirit. Champions continuous improvement and quality.
• Supports corporate efforts for safety, government compliance, and all other company policies and procedures.
• Find A Better Way by championing continuous improvement and quality control efforts to identify opportunities to innovate and improve efficiency, accuracy, and standardization
• Continuously learn and develop self professionally
• Support corporate efforts for safety, government compliance, and all other company policies & procedures
• Perform other related duties as required and assigned

Qualifications

Required:

• Bachelor's degree in Computer Science, Computer Engineering, Information Systems, or related discipline; or equivalent demonstrated experience.
• 4-7 years of professional data engineering experience, with demonstrated technical leadership on enterprise-grade data platforms.
• Advanced proficiency in SQL — including complex query authoring, performance tuning, and schema design at scale.
• Hands-on proficiency with modern data transformation tooling — dbt (preferred), Dataform, or equivalent transformation framework — including modular model design, testing, documentation, and CI/CD integration.
• Hands-on proficiency with workflow orchestration tooling — Airflow, Azure Data Factory, Dagster, or equivalent — including DAG design, dependency management, and operational monitoring.
• Production experience with Snowflake — including warehouse design, role-based access control, performance optimization, and cost management.
• Strong working experience on Microsoft Azure data services — including Azure Data Factory, Azure SQL, Storage, Key Vault, and integration with Azure security and identity services.
• Demonstrated experience designing and operating production data pipelines — including ingestion patterns, transformation frameworks, and observability.
• Hands-on experience using AI development assistants (GitHub Copilot, Microsoft Copilot, Claude, or equivalent) as part of daily development practice.
• Strong working knowledge of data modeling — conceptual, logical, and physical — including dimensional modeling and modern data product patterns.
• Working knowledge of data governance, lineage, and cataloging practices — including data classification, definition stewardship, and metadata management.
• Working knowledge of CI/CD for data — version control, automated testing, environment promotion, and rollback strategies.
• Understanding of data security and compliance frameworks — SOC 2, GDPR, and PII handling principles — with the ability to operate inside a regulated-data environment.
• Proficiency with at least one programming language for data work (Python, Scala, or equivalent) — including for pipeline orchestration, custom transformations, and integration with platform APIs.
• Strong communication and collaboration skills — able to work effectively with engineering peers, business analysts, the Director of Data & Analytics, and cross-functional stakeholders in Product, Client Experience, Sales, and Marketing.
• Demonstrated ability to mentor and grow engineering talent.

Preferred:

• SnowPro Core or SnowPro Advanced certification.
• Azure data certification (e.g., DP-203 Data Engineer Associate).
• Experience with Power BI — including dataset modeling, refresh management, and integration with Snowflake or Azure data sources.
• Experience integrating data platforms with AI/ML services — Azure OpenAI Service, Azure AI Foundry, or vector database technologies.
• Experience with streaming platforms (Kafka, Azure Event Hub, Pub/Sub) and CDC patterns at scale.
• Experience contributing to or operating a data Center of Excellence model — including intake processes, prioritization, and stakeholder management.
• Experience with infrastructure-as-code for data platforms (Terraform, Bicep).
• Experience working in regulated industries or with compliance-sensitive workloads (SOC 2 Type 2, SOX, financial data, regulated commerce).
• Familiarity with data mesh, domain-oriented data product, or lakehouse architectures.
• Experience with observability tooling for data (Monte Carlo, Great Expectations, OpenTelemetry, or equivalent).