Data Engineer

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Teladoc Health

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

🏥 Healthcare

👥 B2C

☁️ SaaS

💰 $80M Post-IPO Debt - Teladoc Health on 2016-07

Healthcare • B2C • SaaS

Teladoc Health is a telehealth company that connects patients with care providers through a virtual care platform offering 24/7 urgent care, primary care, mental health (therapy and psychiatry), chronic condition management (diabetes, hypertension, weight management), specialty consultations, and wellness services. It serves individuals directly and partners with employers, health plans, hospitals and health systems to deliver integrated virtual care solutions and technology at scale.

📋 Description

• Design, build, and maintain batch, incremental, and file-based data pipelines to support analytics, reporting, and operational use cases. • Support data migration initiatives, including the movement of data from legacy systems to modern platforms, ensuring continuity, accuracy, and reconciliation. • Work with enterprise data integration tools such as Talend, including maintaining existing Talend jobs and supporting the migration or re-implementation of Talend pipelines into other platforms where required. • Establish and maintain a robust data engineering environment (e.g., dev/test/prod separation, access controls, naming standards, source control, deployment processes, and monitoring) that enables reliable delivery and safe change management. • Support ingestion, storage, and management of unstructured and semi-structured datasets (e.g., documents, PDFs, text extracts, files, metadata) alongside traditional relational data. • Support and optimize data models in Power BI dashboards and AI-enabled analytics. • Prepare and curate analytics- and AI-ready datasets, including clean, well-defined tables and reference data used in automation and AI-enabled solutions. • Collaborate closely with AI & Automation Specialists to ensure data requirements for AI initiatives are well understood, properly sourced, validated, and production-ready. • Translate business and reporting requirements into scalable data models, transformations, and pipeline designs. • Develop and maintain trusted datasets that support dashboards, scorecards, client reporting, and downstream analytics. • Implement data quality checks, validation logic, reconciliation processes, and monitoring to ensure data reliability and consistency. • Troubleshoot and resolve data issues across ingestion, transformation, and consumption layers, identifying root causes and remediation actions. • Develop and maintain clear documentation, including source-to-target mappings, data definitions, data lineage, and known limitations. • Work in partnership with analytics, IT, data governance, and security stakeholders to ensure data solutions comply with privacy, security, and regulatory expectations. • Contribute to the prioritization, planning, and delivery of multiple data engineering, migration, and enablement initiatives in parallel.

🎯 Requirements

• Bachelor’s Degree in Computer Science, Information Technology, Data Analytics or related field • 4+ year experience working in data engineering, analytics engineering, business intelligence, or data platform roles. • 4+ years Experience with data integration and ETL/ELT tools (e.g., Talend, Azure Data Factory, or similar enterprise platforms). • Experience with Microsoft Power Automate (or similar workflow automation tools) to orchestrate process automation, notifications, and data movement/integration. • Proficiency with Python (or similar scripting languages) for data transformations, automation, API integrations, and lightweight tooling. • Understanding of data orchestration and scheduling patterns, including dependency management, retries, and operational runbooks for production pipelines. • Experience implementing data quality and observability practices (e.g., validation rules, monitoring/alerting, SLAs) to ensure reliable and trusted datasets. • Working knowledge of secure data handling and privacy-by-design principles (e.g., least-privilege access, encryption, PHI/PII considerations) when building and operating data pipelines. • Strong SQL experience for data transformation, validation, reconciliation, and performance tuning. • Experience with Power BI (data modeling, DAX fundamentals, and performance considerations) to support scalable dashboards and self-serve analytics. • Experience working with both structured and unstructured datasets, including file-based or document-oriented data sources. • Familiarity with relational and cloud-based data platforms used for analytics and reporting. • Experience supporting data migrations, legacy system consolidation, or platform modernization initiatives. • Understanding of data quality, documentation practices, and foundational data governance principles. • Strong analytical and problem-solving skills, particularly in diagnosing data quality issues and pipeline failures. • Ability to collaborate effectively with both technical and non-technical stakeholders, including analytics and AI delivery teams. • Strong written and verbal communication skills, with the ability to clearly document data flows, dependencies, and assumptions.

🏖️ Benefits

• Health insurance • 401(k) matching • Flexible work hours • Paid time off • Remote work options