Top Data & Analytics Jobs

Designs scalable, secure cloud data architectures; performs technology proofs of concept, troubleshoots across environments, and migrates BDPass data pipelines to Azure. The role requires expertise in AWS and Azure, data engineering programming, big data technologies, data warehousing, modeling, security, software development, and agile practices, along with strong problem-solving, communication, and collaboration skills.

Design, build, test, deploy, and maintain scalable Azure-based data pipelines and integrations for healthcare analytics. Lead data acquisition, ETL/ELT transformations, data quality detection and remediation, monitoring, documentation, and collaboration with analytics teams to enable BI and advanced analytics while ensuring security and reliability.

Develop and maintain batch and streaming data pipelines supporting analytics and enterprise reporting. Use Python, Spark, Scala, Azure Data Factory, Databricks, Kafka, and cloud storage technologies. Support ETL/ELT, data warehousing, monitoring, incident resolution, security remediation, upgrades, testing, and production stability. Collaborate with cross-functional teams, document solutions, and participate in knowledge transfer while learning enterprise healthcare data systems.

Design, develop, and maintain scalable batch and streaming data pipelines using Python, PySpark, SQL, Databricks, Kafka, and Azure Data Factory. Provide production support, incident resolution, vulnerability remediation, platform upgrades, and reliability improvements. Optimize data models and processing workloads, implement automation and AI-enabled solutions, support CI/CD and cloud modernization, and ensure healthcare data security, governance, and privacy compliance.

Design, develop, and maintain scalable Python-based data pipelines and integrations on Azure (ADF, Databricks, Azure SQL). Implement CI/CD for deployments, model data for BI, optimize performance and cost, document solutions, troubleshoot issues, and collaborate with data scientists and analysts.

Provide technical leadership for analytics engineering across enterprise platforms. Design and optimize scalable Databricks data pipelines using Python, PySpark, and SQL; develop governed Power BI solutions, semantic models, dashboards, and executive reporting. Partner with stakeholders to define metrics, deliver AI-enabled insights, and improve decision-making. Ensure data quality, lineage, security, and governance while optimizing models, queries, and pipelines. Lead large-scale technical initiatives, influence architecture, and guide analytics engineers.

Designs and leads scalable enterprise data architectures and pipelines across Azure, GCP, and Snowflake. Responsibilities include building ETL/ELT frameworks, optimizing Snowflake and distributed processing performance, implementing batch and streaming integrations, and applying data governance, security, and quality standards. The role uses Python, PySpark, Pandas, SQL, Databricks, Airflow, and Hadoop technologies while supporting analytics, AI/ML, and real-time use cases. It also requires cloud platform leadership, cost optimization, stakeholder collaboration, and data strategy execution.

Designs and maintains scalable Power BI dashboards, reports, data models, and SQL solutions using complex healthcare claims, revenue cycle, and 835/837 transaction data. Partners with stakeholders to define KPIs, ensure data quality and governance, translate business needs into actionable insights, and support client, presale, and operational analytics. Collaborates cross-functionally to resolve performance and data issues and improve repeatable analytics processes.

Designs and operationalizes data products supporting analytics, machine learning, generative AI, and agentic AI use cases. The role models and transforms healthcare data, analyzes trends, ensures data quality, supports risk adjustment and customer targeting, and collaborates with data scientists and AI engineers to evaluate models and improve business adoption.

Build and maintain governed Snowflake-based data pipelines and models for performance management insights. Integrate batch and real-time data, implement transformation, quality, lineage, reconciliation, monitoring, and publish-gating controls, and support production readiness. Collaborate with Data Engineering, Product, Data Science, Business Intelligence, Governance, Infrastructure, and business stakeholders on architecture, standards, cloud platforms, CI/CD, orchestration, and reliable analytics capabilities.