Senior Data Engineer

Viventium Software•,

6d•$125,600 - $151,300•Remote

About The Position

An exciting opportunity to join a high-growth organization at the intersection of healthcare and technology. The Senior Data Engineer is responsible for designing, building, maintaining, and optimizing the data platform that powers customer-facing analytics, embedded product capabilities, enterprise reporting, and strategic decision-making across Viventium + Apploi. This role will provide end-to-end ownership of critical components within the Viventium + Apploi data ecosystem, including data ingestion, transformation, modeling, orchestration, security, governance, and platform optimization. The Senior Data Engineer will develop scalable and reliable data solutions that enable business growth, operational excellence, and product innovation while ensuring the highest standards of data quality, performance, and security. The Senior Data Engineer will partner closely with Product, Engineering, Revenue Operations, Customer Success, and business stakeholders to transform complex business needs into trusted, actionable, and scalable data solutions. The ideal candidate thrives in a fast-paced, collaborative environment and brings deep expertise in modern cloud data platforms, strong engineering judgment, a passion for continuous improvement, and a proven track record of delivering high-impact data solutions that drive measurable business outcomes.

Requirements

5+ years of experience designing, building, and supporting production data engineering solutions.
5+ years of hands-on experience with Snowflake and dbt in enterprise environments.
Advanced SQL expertise, including performance optimization, dimensional modeling, window functions, and semi-structured data.
Strong Python development experience for pipeline engineering, orchestration, and automation.
Experience with Prefect, Airflow, Dagster, or similar modern orchestration platforms.
Experience supporting CDC, streaming, event-driven, and API-based data architectures.
Experience utilizing Git, CI/CD, automated testing, and code-review best practices.
Strong understanding of data governance, security, privacy controls, and data quality frameworks.
Exceptional analytical, troubleshooting, and problem-solving capabilities.
Excellent communication and stakeholder management skills within a collaborative, remote-first environment.
Bachelor's degree in Computer Science, Data Engineering, Information Systems, Software Engineering, or a related field preferred.
Equivalent combinations of education, professional training, certifications, and applicable experience may be considered.

Nice To Haves

Experience with Snowflake Cortex, vector search, embeddings, semantic search, or AI-powered data solutions.
Experience within healthcare technology, HCM, HR technology, ATS, or SaaS environments.
Experience designing recommendation engines, matching algorithms, or advanced analytical products.
Experience with dbt package development, Semantic Layer implementations, or macro-intensive dbt architectures.
Experience with geospatial data, dimensional modeling, and reverse ETL technologies.
Snowflake SnowPro certification or comparable advanced technical certifications.

Responsibilities

Design, build, test, document, and maintain scalable data models within a production Snowflake and dbt environment.
Develop data structures across source, staging, transformation, and business-ready analytical layers.
Implement best practices for incremental processing, reusable macros, testing, documentation, and data governance.
Create scalable dimensional and analytical models supporting enterprise reporting, operational analytics, and customer-facing functionality.
Establish standards for data quality, lineage, ownership, validation, and documentation.
Evaluate and continuously improve model performance, scalability, maintainability, and usability.
Design, build, maintain, and monitor end-to-end data pipelines supporting operational databases, third-party systems, APIs, event streams, and other business-critical data sources.
Develop and optimize batch, streaming, and change-data-capture ingestion frameworks.
Build and support ingestion workflows utilizing technologies such as AWS DMS, Estuary, RudderStack, or comparable enterprise platforms.
Implement robust monitoring, validation, reconciliation, retry, and alerting processes.
Investigate and resolve data quality issues, integration failures, latency concerns, and production incidents.
Perform root-cause analysis and implement sustainable corrective actions.
Build and manage data orchestration workflows utilizing Prefect or similar modern orchestration technologies.
Configure deployments, schedules, dependencies, monitoring, retries, and operational controls.
Develop Python-based automation, integrations, and pipeline services supporting enterprise data operations.
Drive workflow efficiency through automation and process optimization.
Maintain appropriate controls across development, testing, and production environments.
Contribute to the continuous improvement of release management and deployment practices.
Partner with Product and Engineering teams to deliver data-powered features that enhance customer experiences and business outcomes.
Translate business and product requirements into scalable, reliable, and maintainable technical solutions.
Support advanced analytical capabilities including entity matching, recommendation engines, search functionality, and scoring models.
Develop solutions leveraging Snowflake Cortex, embeddings, vector search, or AI-powered technologies where appropriate.
Ensure product-facing data solutions meet established standards for performance, security, reliability, explainability, and governance.
Utilize Company-approved AI tools to enhance engineering productivity, code development, testing, documentation, and code review activities.
Validate all AI-assisted outputs for quality, security, functionality, and compliance with engineering standards.
Ensure confidential, proprietary, customer, applicant, and employee information is handled in accordance with Company policies and approved technology practices.
Identify opportunities to responsibly leverage AI and automation to improve development efficiency and operational effectiveness.
Maintain accountability for technical decisions, production solutions, and engineering outcomes.
Implement and maintain secure data management practices utilizing role-based access controls, masking, secure views, and least-privilege principles.
Protect personally identifiable and sensitive data in accordance with Company policies, contractual requirements, security standards, and applicable regulations.
Manage secure customer and partner data sharing using Snowflake shares, reader accounts, customer-scoped views, and approved access mechanisms.
Participate in governance initiatives, security reviews, audit readiness activities, and compliance efforts.
Maintain documentation supporting data access, lineage, ownership, classification, and control processes.
Identify and escalate security, privacy, or data integrity risks in a timely manner.
Monitor and optimize platform performance, reliability, scalability, and cost efficiency.
Evaluate and improve warehouse sizing, query performance, clustering, caching, workload allocation, and resource utilization.
Identify opportunities to improve platform efficiency and operational effectiveness.
Ensure critical processing workloads complete within defined operational windows and service expectations.
Develop monitoring and alerting capabilities that proactively identify risks and issues.
Support incident response, production troubleshooting, and continuous operational improvement.
Promote engineering excellence through code reviews, testing standards, documentation, and CI/CD best practices.
Establish and reinforce standards for data quality, code quality, observability, maintainability, and platform reliability.
Develop automated testing methodologies supporting data integrity and operational confidence.
Provide technical leadership, coaching, and knowledge sharing across the organization.
Contribute to architectural discussions and the long-term evolution of the Viventium + Apploi data ecosystem.
Communicate technical risks, dependencies, tradeoffs, and recommendations effectively.
Foster a culture of ownership, innovation, collaboration, and continuous learning.
Partner with Product, Engineering, Revenue Operations, Customer Success, and business stakeholders to prioritize and deliver impactful data initiatives.
Translate ambiguous business questions into trustworthy and scalable technical solutions.
Communicate effectively with both technical and non-technical stakeholders.
Promote adoption, understanding, and consistent use of data across Viventium + Apploi.
Manage competing priorities with transparency, sound judgment, and proactive communication.