Top Data & Analytics Jobs

Leads enterprise healthcare data engineering strategy, cloud data platform modernization, warehouse and lakehouse architecture, ETL/ELT pipelines, streaming, interoperability, governance, data quality, and master data management. Manages data engineering, analytics, and platform teams while supporting AI/ML initiatives. Requires extensive healthcare data experience, leadership of large programs, cloud and data engineering expertise, knowledge of claims and clinical systems, and familiarity with HL7, FHIR, EHR platforms, and healthcare compliance.

Design and optimize scalable Azure data and AI platforms using Databricks, Spark, Python, and Delta Lake. Build enterprise ETL/ELT pipelines, real-time processing frameworks, feature stores, and model input pipelines. Collaborate with AI teams to operationalize machine learning models, ensure data quality and security, automate engineering workflows, and optimize performance. Participate in Agile development, CI/CD, testing, production support, and on-call operations while developing responsible AI-powered solutions.

Design, develop, and support scalable ETL frameworks and data integration pipelines for complex healthcare datasets in Oracle Exadata environments. Responsibilities include data mapping, transformation, performance tuning, testing, troubleshooting, automation, documentation, data profiling, validation, reconciliation, and quality assurance. The role also supports secure coding standards, code reviews, operational reliability, and integration with modern cloud data platforms.

Lead enterprise data engineering and AI-powered pipeline development, including data ingestion, transformation, ETL/ELT architecture, GenAI and LLM frameworks, vector ingestion, embeddings, governance, data quality, responsible AI, and CI/CD deployment. Collaborate with analytics and AI teams to optimize healthcare data platforms, architecture, and scalable model workflows.

Analyzes financial data and systems, develops SQL queries and stored procedures, supports system implementations and workflows, creates actionable reporting, evaluates process improvements, and communicates financial and operational impacts. The role partners with business and technology teams to refine requirements, troubleshoot systems, recommend changes using analytics, and improve solutions involving SQL, SSIS, Databricks, and Python.

Develop and maintain scalable data pipelines integrating databases, APIs, logs, streaming platforms, and external providers. Build transformation and processing jobs, implement governance and de-identification controls, monitor pipeline reliability, and establish automation and deployment best practices. Partner with analytics, product, infrastructure, and data science teams to advance cloud-based data and analytic platforms using Azure, Databricks, Snowflake, Spark, and SQL.

Develop scalable Azure cloud services and data platforms, including microservices, ETL/ELT pipelines, APIs, and machine learning data solutions. Build and optimize Databricks, Spark, Snowflake, and SQL systems; implement AI and LLM-based workflow automation; improve data reliability and performance; establish data engineering standards; collaborate with stakeholders; and mentor data engineers.

Administer and optimize enterprise SQL Server environments across on-premises and Azure cloud platforms. Responsibilities include high availability, disaster recovery, backups, performance and query tuning, security, indexing, monitoring, automation, SSIS, Azure Data Factory pipelines, and cloud migration support. The role also develops operational runbooks, supports mission-critical production databases, and troubleshoots database, ETL, and cloud integration issues.

Administer, architect, optimize, secure, and automate PostgreSQL, MySQL, and Snowflake databases in Azure. The role develops resilient database solutions for healthcare applications, manages performance, access controls, encryption, backups, and disaster recovery, and creates technical documentation. It collaborates across Scrum teams, presents solutions to stakeholders, evaluates emerging technologies, uses approved AI tools, solves complex database challenges, and mentors junior engineers.

Leads the design and delivery of scalable data platforms and products for healthcare applications. Establishes standards for data modeling, quality, lineage, observability, testing, security, and operational readiness. Partners with product, AI, architecture, governance, and engineering teams to create technical roadmaps, reusable patterns, and multitenant architectures. Enables generative AI and conversational analytics solutions while mentoring engineers and evaluating architectural trade-offs.