Analytics Engineer

• Perform knowledge elicitation from customer subject matter experts and convert that to build analytic solutions
• Design, engineer, and optimize sustainment of large-scale distributed computation platforms and supporting environment (ecosystems) for various stakeholders, business owners, and industry partners
• Oversee the transition of services from third-party vendors to the analytic environment and be responsible for ad hoc and formal end-user training
• Identify applicable data to perform analytics and create solutions to acquire, transform, and load or correlate data components to and from the analytic environment
• Develop custom data modeling procedures to assist with data mining, modeling, and production
• Assess the effectiveness and accuracy of new data sources and data gathering techniques
• Develop processes and tools to monitor and analyze model performance and data accuracy
• Interpret and communicate results to non-technical customers

Data Mining & Analytics Engineer, Journeyman – Security Clearance Required

• Perform knowledge elicitation from customer subject matter experts and convert that to build analytic solutions
• Design, engineer, and optimize sustainment of large-scale distributed computation platforms and supporting environment (ecosystems) for various stakeholders, business owners, and industry partners
• Manage teams of system administrators, prioritize work, identify high-risk critical problems, and dedicate appropriate resources
• Oversee the transition of services from third-party vendors to the analytic environment and be responsible for ad hoc and formal end-user training
• Identify applicable data to perform analytics and create solutions to acquire, transform, and load or correlate data components to and from the analytic environment
• Develop custom data modeling procedures to assist with data mining, modeling, and production
• Assess the effectiveness and accuracy of new data sources and data gathering techniques
• Develop processes and tools to monitor and analyze model performance and data accuracy
• Interpret and communicate results to non-technical customers

Senior Data Mining & Analytics Engineer, Security Clearance Required

• Consult with teams across the organization to translate key product and business processes into technical data requirements. Use dbt, BigQuery, and SQL to build up Packback’s metrics layer to enable self-serve access to core analytics.
• Build, optimize, and orchestrate data pipelines with dbt, Airflow, and Python
• Establish data integrity standards and SLAs to ensure timely, accurate delivery of data
• Build insightful and reliable dashboards to track performance of core company metrics across the product and business
• Collaborate with business operations teams to define and train on best practices with respect to data modeling, analytics, and visualization
• Collaborate with leadership to conduct analyses and apply statistical methods to answer day-to-day questions or support larger initiatives
• Work within Packback’s research function with partner institutions and perform in-depth analyses to understand how Packback’s products are driving key student outcomes
• Consult on the adoption of new technologies that impact Packback’s data infrastructure

• design, build, and own scalable data pipelines that ingest external and internal data into Snowflake, including automation of file-based ingestion processes
• architect, develop, and maintain dbt transformation workflows to produce curated, production-ready data models and tables
• establish and enforce best practices for data modeling, performance optimization, data quality, and pipeline reliability across the platform
• develop and maintain Python- and SQL-based processes for data ingestion, validation, transformation, and monitoring
• lead the implementation and management of GitLab for version control, code collaboration, and CI/CD processes across the analytics team
• define and operationalize team standards for source control, code reviews, deployment workflows, and documentation
• build, optimize, and own Power BI datasets, semantic models, and reporting layers to support scalable analytics and decision-making
• partner with clinical, operational, and business stakeholders to translate data needs into technical solutions and communicate insights effectively
• mentor and train team members on data engineering workflows, Snowflake, dbt, GitLab, and Power BI to enable shared ownership and reduce single points of failure
• continuously improve platform architecture, tooling, and processes to support a scalable, maintainable, and modern analytics ecosystem
• support expansion of Snowflake capabilities (e.g., application layers or integrations) to enable advanced analytics and operational use cases
• contribute to advanced analytical initiatives, including identifying cost-savings opportunities and generating insights from complex healthcare datasets

Senior Analytics Engineer

• Apply hands on analytics and data expertise to solve complex, fast moving financial and operational problems in the health tech space
• Design, develop and maintain scalable, analytics ready data models (primarily in dbt) that power a PBM and care navigation data ecosystem that support client reporting, performance analytics, operational analysis and self-service business intelligence.
• Translate complex pharmacy benefits and healthcare navigation workflows, business rules and operational processes into transparent, maintainable, and auditable data models.
• Partner closely with data and analytics engineers, data analysts and business stakeholders to deliver reliable data products that can be leveraged across multiple use cases and continuously optimize data models and warehouse performance to support large-scale PBM/care navigation datasets and growing business needs.
• Implement automated data quality checks, testing frameworks and reconciliation processes to ensure data reliability.
• Establish documentation standards, lineage and analytics engineering guardrails that promote transparency and auditability.
• Contribute to engineering best practices including version control, CI/CD, incremental models, code reviews, and observability.
• Contribute to data governance by establishing modeling standards, documentation and guardrails that support auditability, explainability and long term maintainability.
• Build data foundations that enable future agentic analytics engineering use cases, including self-service analytics and AI-assisted insight generation.
• Stay informed on emerging technologies and identify opportunities to incorporate AI into analytics engineering workflows.

Analytics Engineer, Platform

• Execute platform administration and reporting delivery for customer analytics
• Build reporting views and dashboards for stakeholders
• Configure and maintain NICE CXone Interaction Analytics assets
• Perform validation, tuning, and troubleshooting of analytics outputs
• Support governance, documentation, and quality standards for analytics

Contact Center Analytics Developer

• Design and develop data serving and semantic layers to support business needs ranging from traditional reporting to reverse ETL and LLM/AI implementations.
• Develop data models for use by analysts and applications from across the business using modern dimensional modeling practices to efficiently serve accurate, consistent data.
• Build and maintain robust, scalable data pipelines to extract, transform, and load (ETL, ELT) data from various sources into the data platform using both batch processing and event-driven patterns.
• Create efficient, performant data models to support business requirements and analytics needs.
• Implement and automate data quality checks and monitoring to ensure data accuracy and drive observability.
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions and on-premises systems.
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis.
• Work closely with data analysts, data scientists, and business stakeholders to understand their data needs and deliver solutions.
• Identify and resolve data-related issues and challenges for technical and non-technical stakeholders.
• Support company-wide projects ranging from server migrations to mergers and acquisitions as a data and systems expert.
• Automate data processes to improve efficiency and reduce manual effort.
• Implement modern concepts like metadata-driven pipelines and Infrastructure-as-Code that will play a key role in a scalable, resilient data platform.
• Document data processes, architecture, and workflows.

• Collaborate with stakeholders to understand business requirements and translate them into scalable and efficient data analytics solutions.
• Possess in-depth knowledge and hands-on experience with Microsoft Fabric ecosystem (Lakehouse, PySpark/SQL notebooks, data pipelines, and deployment pipelines).
• Understanding Microsoft Dynamics as a data source a big plus.
• Design data models that bridge the Fabric Lakehouse, Power BI semantic models, and reporting layers, ensuring consistency between raw, curated, and business-facing data.
• Optimize data models to support efficient data storage, retrieval, and analysis, ensuring data integrity, accuracy, and reliability.
• Knowledge of Fabric capacity (CU) optimization a big plus.
• Implement data integration pipelines using Microsoft Fabric and other relevant tools to extract, transform, and load data from various sources into a unified data platform.
• Create visually appealing and interactive dashboards, reports, and visualizations using Microsoft Power BI and DAX language, enabling users to gain valuable insights from data.
• Identify performance bottlenecks and implement optimizations to improve data processing, query performance, and overall system efficiency.
• Collaborate with cross-functional teams, providing technical leadership and guidance to data engineers, analysts, and other stakeholders to ensure successful project delivery.
• Keep abreast of the latest advancements in data analytics technologies (Microsoft based and others) and industry trends, continuously enhancing your expertise and applying best practices in solution design and implementation.

Data Analytics Engineer

• Architect, Design and implement ETL processes.
• Develop and maintain integration tools and utilities.
• Proficient in writing SQL queries and troubleshooting issues.
• Support existing applications.
• Provide guidance and support to Junior/Mid-level developers.
• Instruct, direct, and check the work of others.
• Lead, design, code, test and debug complex development efforts.
• Document, enforce and model best standards and practices to others.
• Oversee project completions and ensure user satisfaction.
• Complies with Company policies, procedures, regulatory requirements, and complete mandatory company compliance training by prescribed deadlines.
• Ability to accept guidance and supervision from management.
• Other duties that may be reasonably assigned.

Analytics Engineer jobs in Florida

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Data Mining & Analytics Engineer, Journeyman – Security Clearance Required

Senior Data Mining & Analytics Engineer, Security Clearance Required

Analytics Engineer

Senior Analytics Engineer

Analytics Engineer, Platform

Contact Center Analytics Developer

Analytics Engineer

Data Analytics Engineer

Senior ETL Developer