Analytics Engineer

• Design and develop data integrations to deliver Enterprise BI Data Marts and BI Semantic Data Layer(s)
• Develop modern data engineering pipelines leveraging AI assisted capabilities, such as Databricks GenAI features
• Lead multiple projects, provide technical guidance, and support other team members within the DAS organization
• Improve data processes, technology infrastructure, and reporting and analytical services

Lead Analytics Engineer

• Own end-to-end implementations for the data products for our external customers.
• Set technical direction and standards for the analytics engineering team.
• Build and maintain scalable data pipelines and analytics models using Python, DBT, and SQL.
• Create and manage dashboards in our external and internal analytics products so stakeholders have actionable insights.
• Prototype new data products to add to our product suite.
• Collaborate with engineering to drive the roadmap for the data products.
• Mentor engineers and own their professional development.
• Use GitHub-based workflows to maintain clean, version-controlled analytics code.
• Act as a subject matter expert for analytics best practices.

Staff Analytics Engineer

• Design, build, and maintain end-to-end data pipelines — from raw source ingestion through transformation, modeling, and delivery — with reliability and performance in mind
• Develop and extend dbt models on top of Acrisure's data warehouse, with particular focus on the operational and SSOT data layers that underpin enterprise reporting
• Build Palantir Foundry applications, Ontology objects, and workflows that surface data to analysts and business stakeholders in forms they can act on moving the team beyond ad hoc querying toward scalable, reusable tooling
• Partner closely with analysts and business intelligence teammates to understand downstream needs and engineer upstream data assets accordingly
• Uphold data modeling best practices in the work you touch: clear documentation, version control via Git, incremental build strategies, and data quality testing
• Leverage AI tools (Claude, Palantir AI FDE, Gemini, and others) to accelerate development, while applying rigorous critical evaluation to any AI-generated code or logic before it ships
• Contribute to the pod's overall data strategy, surface infrastructure gaps, raise architectural improvements, and help shape how the Growth & Placement pod's data layer evolves

Analytics Engineer, Growth & Placement

• Design, develop, and support artificial intelligence, intelligent automation, data engineering, analytics, and operational intelligence solutions across Clean Energy Fuels.
• work across the full solution lifecycle—from business requirements and architecture through development, deployment, and support

Senior Data Science, AI & Analytics Engineer

• 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

• Establish and execute engineering objectives for digital analytics and tag management systems and CX platforms in alignment with business and technology strategies
• Operate as a technical leader in designing, developing, implementing, and supporting scalable and reliable analytics engineering solutions that meet defined marketing and digital needs
• Provide thought leadership in digital data collection, aligning strategies with business goals and enterprise technology standards
• Work closely with cross-functional teams to bring solutions to life
• Provide technical direction and guidance on architecture, design patterns, and engineering standards within the marketing and digital domains
• Ensure engineering work adheres to best practices for quality, security, performance, and maintainability
• Establish engineering best practices for mobile app analytics, data privacy, and testing automation
• Conduct code reviews to provide guidance on on engineering best practices and compliance with development procedures
• Accountable to ensure all aspects of product development follow compliance and security best practices
• Embraces emerging technology opportunities (such as automation, AI) and contributes to the best practices in support of the bank’s technology transformation
• Contributes to a culture of innovation, collaboration, and continuous improvement

Senior Software Engineer, Digital Analytics

• Design, develop, and maintain Databricks data pipelines and curated datasets supporting reporting and analytics initiatives.
• Build and optimize analytics-ready datasets while ensuring data quality through validation, monitoring, and reconciliation processes.
• Partner with analytics and reporting teams to align data structures with business and reporting requirements.
• Support production operations and deployments across multiple environments, including troubleshooting data and performance issues.
• Develop and maintain technical documentation such as data mappings, transformation specifications, and operational procedures.
• Provide technical leadership and delivery support through solution recommendations, code reviews, testing, and release readiness activities.

• Developing novel, parallel algorithms to accelerate core problems in data processing and power the next generation of data center workflows
• Implementing solutions in C++ and CUDA
• Contributing to open source projects, such as cuDF, Velox, Presto and Spark
• Benchmarking, profiling, and optimizing code
• Working closely with the world’s top experts in GPU hardware, software and workflows

Senior Software Engineer, C++, CUDA – Analytics, Data Intelligence

• Creates and leads the technical design and development of software solutions.
• Proposes & designs software solutions to address complex business needs.
• Prepares technical and procedural documentation required.
• Facilitates complex problem resolution.
• Provides technical guidance and support to colleagues.
• Reviews coding, testing, and documentation of software.
• Applies modern principles, methodologies and tools to advance business initiatives and capabilities.

Software Engineer Lead – ETL

• Design, develop, and maintain Databricks ETL/ELT pipelines to process Medicaid claims, eligibility, encounters, provider files, and supplemental datasets
• Ingest data from MMIS systems, state data warehouses, managed care organizations, and other Medicaid program data sources
• Implement transformation, validation, and enrichment logic aligned to Medicaid data quality standards
• Optimize Spark jobs for performance and cost efficiency across large Medicaid datasets
• Build and maintain Data Lake models for auditability, lineage, and compliance
• Develop Power BI semantic models, dashboards, and reports that support Medicaid operations
• Collaborate with Medicaid program staff, policy teams, actuaries, and data governance groups

Senior Power BI, Databricks ETL Engineer

• Architect, build and maintain efficient and reliable data products (dbt models, pipelines, Looker explores, visualizations, etc.) that enable data driven decision making.
• Build and maintain ELT pipelines from source systems to data warehouses.
• Influence strategy and drive execution of our analytical roadmap delivering self-service business intelligence at scale.
• Provide leadership and mentorship to other engineers fostering collaboration, knowledge sharing and a high bar for quality.
• Improve end users trust and confidence by driving best practices with regards to data discoverability, observability, lineage and stewardship.
• Contribute towards a robust data security strategy protecting sensitive information and content.
• Solve critical business problems through the application of programming, optimization, automation, and analytical skills.
• Drive functional strategy for AI adoption, identifying transformational opportunities that create competitive advantage in how we build and scale our data products.
• Build scalable AI-powered systems that fundamentally change how analytical work gets done across multiple teams.
• Serve as a trusted advisor to leadership on AI capabilities, limitations, and strategic implications for our data platform.
• Establish governance frameworks and best practices for responsible AI usage at scale within the analytics function.
• Transform core data and analytics processes through strategic AI integration—including intelligent pipeline monitoring, automated data quality checks, and AI-assisted insight generation.
• Quantify AI impact on key data metrics and make data-driven recommendations for continued investment.

Senior Analytics Engineer

• Design and develop data integrations to deliver Enterprise BI Data Marts and BI Semantic Data Layer(s)
• Develop modern data engineering pipelines leveraging AI assisted capabilities, such as Databricks GenAI features and developer productivity tools (e.g., GitHub Copilot)
• Lead multiple projects, provide technical guidance, and support other team members within the DAS organization
• Improve data processes, technology infrastructure, and reporting and analytical services

• Build the core analytics data model – Design and maintain scalable fact and dimension models in our cloud data warehouse that support enterprise reporting and analysis.
• Create a trusted reporting layer across business systems – Integrate and model data from ERP, financial, project management, and operational platforms (e.g., NetSuite, Procore, Epicor, Snowflake, Timestream) to create a consistent source of truth.
• Preserve critical business context across systems – Identify where important financial or operational detail is lost in source systems or handoffs, and design data structures that retain the fidelity needed for analysis.
• Establish modeling standards and analytics best practices – Help define scalable approaches to naming, structure, metric logic, testing, and maintainability across the analytics environment.
• Deliver meaningful reporting and dashboards – Build and maintain dashboards and semantic models in our BI tooling that give stakeholders visibility into financial performance, project execution, and operational KPIs.
• Partner directly with stakeholders to solve ambiguous problems – Translate real business questions – such as cost overruns, efficiency gaps, and project performance – into clear datasets, metrics, and reporting solutions.
• Support planning and forecasting workflows – Maintain and improve planning and budgeting environments to help align planning, forecasting, and actual performance.
• Standardize KPIs across teams – Work with Finance, IT, Continuous Improvement, Operations, and other functions to define, document, and govern consistent metrics across the organization.

Senior Data Analytics Engineer

• Define, prototype, and productionize safety and performance metrics for automated driving systems.
• Establish analytic approaches and sufficiency criteria that support safety assessment, development decisions, and launch readiness.
• Support proactive safety monitoring and targeted investigations tied to specific system-performance or safety questions.
• Apply engineering and physics-based methods to process raw signals and derive meaningful representations of vehicle motion, driving context, and system behavior.
• Create interactive visualizations and reporting artifacts that communicate safety insights clearly, enhance transparency, and reduce barriers to interrogating source data in support of technical decision-making.

Analytics Engineer, AV Safety Engineering

• 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.

• Analyze sales, repair, and upgrade performance data to identify trends, opportunities, and risks across the US & Canada field organizations
• Design and develop dashboards, reports, and data visualizations that translate complex data into clear, actionable insights for Sales, Field Operations, and Leadership
• Develop working fluency in the elevator business: unit profiles, component knowledge, contract basics, and the KPIs that drive rep behavior
• Build and maintain data models that support long ‑ term strategy for repair revenue growth, customer conversion, and sales productivity
• Conduct deep ‑ dive analytics on customer behavior, portfolio performance, and service history to recommend targeted upgrade and repair strategies
• Support business case development for new repair programs, pricing initiatives, and process improvements using data ‑ driven insights
• Ensure data accuracy, governance, and integrity across multiple sources
• Contribute to metadata documentation efforts
• Evaluate business processes to identify areas for AI workflow optimization

Business Analytics Engineer

• Build, maintain, & optimize ETL data pipelines
• Build, maintain, & optimize corporate semantic models
• Address IT tickets & troubleshoot system performance
• Perform administrative duties to maintain the current data environment
• Assist, as needed, with the development of reports and other solutions
• Work with stakeholders throughout solution lifecycle
• Improve platform stability, data value & quality, and promote a data culture
• Actively collaborate with teammates to share workloads, develop skills, & strengthen the team

• 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 Texas

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Lead Analytics Engineer

Staff Analytics Engineer

Analytics Engineer, Growth & Placement

Senior Data Science, AI & Analytics Engineer

Analytics Engineer

Senior Software Engineer, Digital Analytics

Analytics Engineer

Senior Software Engineer, C++, CUDA – Analytics, Data Intelligence

Software Engineer Lead – ETL

Senior Power BI, Databricks ETL Engineer

Senior Analytics Engineer

Lead Analytics Engineer

Senior Data Analytics Engineer

Analytics Engineer, AV Safety Engineering

Analytics Engineer

Business Analytics Engineer

Analytics Engineer

Data Analytics Engineer

Senior ETL Developer