• Lead end-to-end data migration activities, including planning, mapping, transformation, validation, and reconciliation. • Define and execute data migration strategies and methodologies for Skadina implementations. • Oversee functional, integration, system, regression, and User Acceptance Testing (UAT) activities. • Ensure data quality, integrity, and consistency throughout migration and deployment phases. • Develop and review test plans, test cases, test scripts, and quality metrics. • Coordinate defect management, issue resolution, and root cause analysis activities. • Collaborate with architects, business analysts, developers, and business stakeholders to ensure successful project delivery. • Support cutover planning, deployment activities, and post-go-live stabilization. • Monitor project quality standards and ensure compliance with established processes and best practices. • Provide technical leadership and mentorship to project teams during migration and testing phases. • Prepare and maintain technical documentation, migration plans, and testing reports.

Role Description Enable Data is seeking an experienced Azure Data Architect with strong hands-on engineering expertise to design, develop, optimize, and modernize enterprise-scale data platforms. The role combines solution architecture, technical leadership, and individual contributor responsibilities, requiring deep expertise in Azure data services, data engineering best practices, and large-scale data processing using PySpark, Python, SQL, and Azure Databricks. The ideal candidate will be responsible for analyzing existing data products and pipelines, identifying improvement opportunities, leading refactoring and redesign initiatives, and implementing scalable, reliable, and high-performance data solutions on Azure. Responsibilities - Architecture & Solution Design - Overall 15+ years of experience with 10+ years of experience in Data Engineering and Analytics solutions. - Design end-to-end data architectures on Microsoft Azure. - Define data ingestion, transformation, storage, governance, and consumption strategies. - Create scalable, secure, and cost-effective data solutions aligned with business objectives. - Establish architectural standards, design patterns, and best practices for data engineering teams. - Collaborate with business stakeholders, product owners, and technical teams to translate requirements into technical solutions. - Design, develop, and modernize enterprise data platforms on Azure. - Analyze, refactor, and redesign existing data pipelines and products while building new scalable data solutions. - Act as a hands-on individual contributor with technical leadership responsibilities, including mentoring junior developers and driving best practices. - Strong hands-on experience in PySpark, Python, and SQL. - Expertise in Azure Databricks and Azure data ecosystem. - Experience designing and implementing scalable data architectures. - Ability to analyze, optimize, refactor, and redesign existing data pipelines. - Develop and maintain ETL/ELT solutions and data products. - Performance tuning, troubleshooting, and data quality implementation. - Mentor and guide junior developers and conduct code reviews. - Collaborate with cross-functional teams to deliver end-to-end data solutions. Requirements - Azure Data Lake, Azure Data Factory, Azure Synapse. - Data warehousing, lakehouse architecture, and cloud migration. - CI/CD, Git, and Agile development practices. Role Type - Senior Individual Contributor with Technical Leadership responsibilities.

• Architecture & Solution Design • Overall 15+ years of experience with 10+ years of experience in Data Engineering and Analytics solutions. • Design end-to-end data architectures on Microsoft Azure. • Define data ingestion, transformation, storage, governance, and consumption strategies. • Create scalable, secure, and cost-effective data solutions aligned with business objectives. • Establish architectural standards, design patterns, and best practices for data engineering teams. • Collaborate with business stakeholders, product owners, and technical teams to translate requirements into technical solutions. • Design, develop, and modernize enterprise data platforms on Azure. Analyze, refactor, and redesign existing data pipelines and products while building new scalable data solutions. • Act as a hands-on individual contributor with technical leadership responsibilities, including mentoring junior developers and driving best practices. • Strong hands-on experience in PySpark, Python, and SQL • Expertise in Azure Databricks and Azure data ecosystem • Experience designing and implementing scalable data architectures • Ability to analyze, optimize, refactor, and redesign existing data pipelines • Develop and maintain ETL/ELT solutions and data products • Performance tuning, troubleshooting, and data quality implementation • Mentor and guide junior developers and conduct code reviews • Collaborate with cross-functional teams to deliver end-to-end data solutions

• Own the product roadmap for Arbital’s data pipeline platform, including ingestion, transformation, calculation, validation, audit trail, and AI consumption layers • Define and prioritize pipeline capabilities based on client needs, implementation learnings, engineering constraints, and long-term platform scalability goals • Translate complex healthcare data requirements from claims processing to VBC contract logic into structured, buildable product specs • Partner with leadership to align pipeline investments with Arbital’s broader product and go-to-market strategy • Write detailed PRDs, user stories, and technical specifications for platform features, configurations, and automation tooling • Work directly with engineering to scope, sequence, and ship pipeline improvements — balancing speed, quality, and flexibility • Define acceptance criteria and lead QA processes for new pipeline & platform capabilities, ensuring outputs meet accuracy and performance standards • Drive platform delivery end-to-end, owning prioritization, cross-team dependencies, and release coordination • Develop deep fluency in Arbital’s data models, pipeline architecture, and healthcare data standards (claims, eligibility, attribution, CMS/ACO files), and actuarial concepts (IBNR, rebates, contract terms) • Work hands-on with data — running SQL queries, reviewing pipeline outputs, and validating logic — to inform product decisions and support debugging • Define standards for data quality, deduplication, business rule configuration, lineage, and pipeline observability across all client environments • Evaluate and recommend tooling improvements across the platform stack (e.g., Airflow, Databricks, AWS) in partnership with engineering • Serve as the primary product owner for data capabilities across implementation, engineering, actuarial, and data science teams • Partner closely with the Implementation team to surface recurring client configuration needs and turn them into scalable platform features • Collaborate with actuarial and data science teams to ensure pipeline logic correctly supports attribution, aggregation, and actuarial use cases • Communicate roadmap priorities, tradeoffs, and delivery status clearly to both technical teams and non-technical stakeholders