Data Engineer

• Design, develop, and maintain scalable ELT/ETL pipelines using Databricks, Apache Spark, SQL, and Python
• Build and maintain reliable, high-quality datasets that support enterprise analytics, operational reporting, and AI use cases
• Develop data transformations using Delta Lake and Lakehouse architecture best practices
• Integrate data from enterprise applications, APIs, databases, files, and other internal and external sources
• Collaborate with business stakeholders, analysts, and product teams to understand data requirements and deliver scalable solutions
• Monitor, troubleshoot, and optimize production data pipelines for reliability, scalability, and performance
• Participate in migrating and modernizing legacy data processes to cloud-native architectures
• Improve data quality through validation, testing, monitoring, and automation
• Contribute to engineering best practices through code reviews, documentation, and knowledge sharing
• Support continuous improvement of the enterprise data platform by identifying opportunities for automation, optimization, and simplification

Data Engineer II

• Lead a team of 2-3 engineers
• Working with fellow Nava engineers to design, review, and build well-crafted software
• Working in an agile manner to efficiently ship new features that meet user needs
• Design and develop scalable data ingestion and processing pipelines
• Implement large-scale data ecosystems within cloud-based platforms that include data management and data governance of structured and unstructured data
• Implement data validation and quality checks to ensure accuracy and consistency
• Work with cross-functional project teams to gather business requirements and translate to detailed technical specifications
• Work with Government partners to assist and develop data engineering applications and pipelines that will enable data services and processing capabilities
• Participate in software design and code reviews
• Maintain security and privacy standards in all aspects of the data pipeline
• Taking part in hiring activities (e.g., submitting referrals, conducting interviews, and attending interview debriefs), as needed

Senior Software Engineer, Data

• Design and implement the canonical data model for Vultr’s control plane ERP transformation: product catalog, chart of accounts, financial transactions, cost centers
• Build and maintain the internal APIs and event streams that Finance, Analytics, and downstream systems consume
• Refactor existing administer code toward clean domain boundaries (no more conflated orders/ledger/report tables)
• Implement data upgrade/migration paths from legacy schemas without breaking production
• Write the integration layer between administer and Vultr's existing billing, provisioning, and reporting systems
• Establish data validation and integrity checks across the financial data layer
• Collaborate with the Product Manager to translate finance domain requirements into durable schema designs
• Document architectural decisions and data model contracts for downstream consumers

Senior Software Engineer, ERP, Data & Domain

• Own the path from raw transactional and event data to trustworthy, well-modeled datasets powering BetMGM's analytics, ML, and operational systems.
• Design, build, and operate batch, micro-batch, and streaming pipelines feeding Snowflake — Prefect-orchestrated flows on ECS Fargate, dbt for transformation, Snowpipe Streaming and Kafka for event ingestion.
• Own the full dbt lifecycle (sources → staging → intermediate → marts) with model contracts, freshness SLAs, automated tests, and version-controlled documentation.
• Stand up Snowflake objects (warehouses, RBAC, resource monitors, Dynamic Tables, Iceberg tables) through Terraform — no ClickOps in production.
• Build AWS-native infrastructure for data workloads — S3, ECS Fargate, Lambda, EMR Serverless, Glue Catalog, IAM, Secrets Manager, VPC endpoints — entirely in Terraform.
• Maintain CI/CD pipelines (GitLab CI or GitHub Actions) that gate every change with linting, dbt build, unit tests, contract checks, and AI-assisted code review.
• Tune warehouse sizing, clustering, and query patterns for cost and latency; instrument credit usage via ACCOUNT_USAGE; right-size before scaling up.
• Design RBAC, masking policies, and row-access policies that satisfy a regulated operator without becoming an access bottleneck.
• Own freshness SLAs and data contracts for the gold layer; configure Monte Carlo coverage for volume, freshness, schema, and distribution; triage incidents end-to-end.
• Collaborate with analytics engineers, data scientists, and ML platform engineers on shared standards (naming, testing, observability, lineage, cost attribution).

Senior Data Engineer

• Own the building, maintenance, and optimization of pipelines to ingest data from both operational databases and third-party tools into a data lake/warehouse.
• Architect highly efficient ingestion patterns that handle evolving data schemas and high-volume, multi-source data streams seamlessly.
• Optimize pipeline performance to ensure maximum uptime, high throughput, and cost-effective compute usage.
• Use dbt to transform raw data within the data warehouse into structured, production-ready schemas.
• Write templated SQL and Jinja code to enforce macro-driven, modular, and DRY (Don't Repeat Yourself) development practices.
• Perform rigorous data quality checks by implementing native dbt tests.
• Set up monitoring and alerting frameworks around both ingestion routines and dbt builds.
• Track pipeline health metrics to measure and report on overall data freshness and platform reliability.
• Build data applications that interact with the data warehouse to empower decentralized self-service analytics.
• Build internal tooling and libraries to facilitate analytics and ML work.

Staff Data Engineer

• Designs and develops moderately complex queries, objects, ETL packages, reporting structures, and other objects necessary to support detailed analysis and self-service reporting needs.
• Collaborates with analytics and business teams to create and improve data models that feed BI tools, increasing data accessibility, and fostering data-driven decision making across the organization.
• Takes ownership of the health, reliability, and security of the data platform by designing and implementing processes and solutions that proactively monitor data delivery across the full pipeline — ensuring data is accurate, complete, consistent, and timely from source ingestion through gold layer curation.
• Investigates, diagnoses, and resolves complex data pipeline failures across all system layers; uses findings to drive pipeline improvements and communicates status, root cause, and resolution clearly to the team throughout the full lifecycle of an incident.
• Assists with the development and delivery of data solutions, including data pipelines, API integration, and other structures and processes to support the data platform
• Identifies, designs, and implements internal process improvements, including the automation of manual processes, optimization of data delivery, and refactoring of code for improved performance
• Builds pipelines to enable the optimal extraction, transformation, and loading of data from a wide variety of data sources
• Provides strategic assistance to BI leadership and the BI team by making recommendations in the areas of data management and best practices
• Collaborates with developers, database administrators, data architects, and others on reporting and data management projects as needed
• Takes on other duties needed to help drive our Purpose, fulfill our Brand Principles, and abide by our Organization’s Values

• Design, build, and optimize robust ETLs using PySpark and DBT to process large-scale customer datasets.
• Define the technical vision for DC data architecture, mentor engineers, and manage external contractors to ensure the team delivers high-quality, practical solutions for current and future needs.
• Partner with product, engineering, and applied science teams to scope work and deliver data solutions that address real-world challenges in customer data quality and product feature requirements.

Staff Software Engineer – DC Tech Lead

• Collaborate with other Engineers and Architects to define the enterprise data architecture roadmap.
• Drive the design and optimization of OLTP and OLAP models for scalability, performance, and maintainability.
• Establish standards for data modeling, storage, and integration across systems.
• Partner with other key stakeholders within engineering in developing a comprehensive data and BI strategy aligned with business objectives.
• Define data governance practices, data access control, compliance, and quality standards.
• Lead proof-of-concepts (POCs) for new data technologies and frameworks.
• Own the architectural review process and guidance to development teams on data-related projects.
• Establish and ensure best patterns and practices for ORM usage (Entity Framework and others) and SQL optimization, according to business needs.
• Evaluate and recommend where AI/ML can enhance data-driven decision-making.
• Stay current with modern data engineering trends, cloud-native architectures, and big data platforms.
• Actively participate and take ownership of Data Architecture artifacts via ADR process.
• Drive AI adoption by introducing and guiding teams in leveraging AI capabilities within the data engineering space to enhance internal efficiency, innovation, and decision-making in everyday workflows.
• Liaison between technology and product teams assisting teams in translating business requirements into tangible architectures that meet business needs now in and in the future.
• Design data architectures that accurately reflect and support evolving complex product and business processes and are required by our customer base.
• Lead data architecture initiatives, collaborating with cross functional teams, from concept through implementation, including governance and quality frameworks.
• Identify opportunities for predictive analytics and advanced modeling.
• Support and promote the company values through positive interactions with both internal and external stakeholders on a regular basis.
• Other strategic business initiatives or special cross-functional project involvement as required.

Data Architect

• Lead a distributed engineering team across platform/product engineering, connectors, QA, DevOps/infra, AI implementation, data governance, and support.
• Own delivery, quality, release discipline, and execution of the technical roadmap.
• Install engineering discipline where it's thin: automated testing, QA, CI/CD, release governance, and versioning.
• Drive repeatable, perimeter-safe deployments, including containerization, infrastructure-as-code, secure deployment, and SOC 2 readiness.
• Partner with the Chief Architect on the connector framework, canonical metadata model, architecture decisions, and product IP.
• Build the team: assess current talent, retain the strong, hire the gaps, and align the team plan to the roadmap.
• Push practical AI use across coding, review, testing, ops, and engineering productivity.

Director of Engineering – Data Platform

• Lead the end-to-end design, development, deployment, and support of scalable, production-grade data integration and engineering solutions
• Create and maintain technical designs and documentation aligned with business requirements, data architecture, and enterprise standards
• Collaborate with technology leadership to evaluate and implement new tools, technologies, and best practices that improve platform performance and user experience
• Partner with Product Management and business stakeholders to deliver data solutions that meet business objectives and design requirements
• Build strong cross-functional relationships, effectively communicating technical solutions and driving stakeholder alignment
• Mentor junior engineers, guide architectural decisions, and balance technical tradeoffs to deliver reliable and intuitive data products
• Drive operational excellence through monitoring, optimization, troubleshooting, and ongoing support of data platforms and services

Lead Data Engineer

• Define and evolve enterprise data architecture principles, standards, and build specifications.
• Produce detailed technical development artifacts including data flow diagrams, process flows, and system interactions.
• Partner with collaborators to map processes, data flows, and system dependencies end-to-end and translate complex business processes into clear data and process flow builds.
• Design end-to-end data lifecycle within the enterprise data platform, from source systems through standardization to consumption.
• Develop and maintain conceptual, logical, and physical data models that align with enterprise standards.
• Establish and enforce data modeling standards, naming conventions, and documentation practices.
• Define and manage data abstraction layers (conceptual, logical, physical, semantic).
• Apply guidelines across 3NF, dimensional, and Data Vault modeling approaches.
• Ensure alignment between business definitions, data structures, and downstream consumption needs.
• Build and maintain canonical data models to enable consistency across systems and domains.
• Define enterprise integration patterns for data sharing (cloud share, batch, streaming, APIs, event-driven) and their appropriate use.
• Architect for interoperability across platforms, enabling decoupled and reusable data assets.
• Define data contracts and interface specifications between producers and consumers.
• Make and detail architecture trade-offs (e.g., normalization vs usability, latency vs consistency).
• Promote data quality, lineage, and governance throughout data flows.
• Perform data analysis and data profiling to validate assumptions and inform builds.
• Guide and mentor team members on architectural standards and technical build guidelines.

Enterprise Data Architect

• Serves as a lead in the analysis of business requirements associated with the design and implementation of highly complex, highly scalable enterprise data processes
• Designs and develops robust and efficient processes for extracting, loading and transforming data to meet the needs of enterprise scale projects
• Provides guidance and participates in activities involved in the analysis, design, development, troubleshooting, debugging and maintenance of an enterprise data management and data ecosystem
• Identifies, investigates, tracks, and resolves the most complex technical errors ensuring the solution is timely and of acceptable quality
• Establishes and enforces database standards, guidelines, procedures and quality assurance for database deliverables such as conceptual design, logical data model capacity planning, external data interface specs, data loading, data maintenance plan and security
• Reviews and analyzes data objects and models to improve data management and data access
• Designs, constructs, tests and implements extraction and transformation programs, processes and cycles for historical and current source data
• Creates technical specification for supporting enterprise data transformation processes and supplementing system documentation for ETL processes
• Performs data profiling, source-target mappings, data transformation development, optimization, testing and implementation
• This position participates and serves as a lead in maintaining the ETL environment for the company’s system-wide enterprise data warehouse.

Supply Chain Data Engineer

• Design and develop data serving and semantic layers to support business needs
• Develop data models for use by analysts and applications using modern dimensional modeling practices
• Build and maintain robust, scalable data pipelines using ETL, ELT processes
• Create efficient and performant data models to support business requirements
• Implement and automate data quality checks and monitoring to ensure data accuracy
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis
• Collaborate with data analysts, data scientists, and business stakeholders to understand their data needs

Data Engineer jobs in Nevada

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Data Engineer II

Senior Software Engineer, Data

Senior Software Engineer, ERP, Data & Domain

Senior Data Engineer

Staff Data Engineer

Data Engineer II

Staff Software Engineer – DC Tech Lead

Data Architect

Director of Engineering – Data Platform

Lead Data Engineer

Enterprise Data Architect

Supply Chain Data Engineer

Data Engineer