Top Data Engineer Jobs in Fayetteville, AR

Build and maintain a near real-time financial dashboard by integrating market and forecast APIs, automating financial models, and crafting clear visual narratives using Flourish and Canva. Collaborate with design and marketing partners, ensure data accuracy and performance, and optimize data flows across multiple feeds.

Lead the design and ownership of Jane's customer data infrastructure: event collection, pipelines, CDP tooling, schema governance, identity resolution, and attribution. Define instrumentation standards, ensure consent-compliant tracking in a healthcare context, evaluate/migrate CDP tooling, and collaborate cross-functionally with marketing, BI, product, and engineering.

Lead design and implementation of scalable, cost-optimized data pipelines and real-time streaming solutions. Drive data architecture, governance, security (row/column-level), ETL orchestration, and enterprise visualization. Mentor junior engineers, engage vendors, optimize cloud costs, and collaborate with stakeholders to deliver strategic, data-driven insights for logistics operations.

Design, build, and maintain scalable Databricks-based data pipelines and ELT workflows; develop and document dbt models; define data modeling methodology; ensure data quality, reliability, and scalability; collaborate with clients and cross-functional teams; provide technical guidance and deliver improvements to clients' data infrastructure and analytics capabilities.

As a Senior Data Engineer, you will manage data pipelines, ensure data quality, partner with teams for data-driven decisions, and improve the data platform while maintaining data integrity.

The Senior Data Engineer will create and manage data pipelines, ensuring data quality, collaborating with teams for data-driven decisions, and evolving the data platform.

Lead design and evolution of Foodsmart's data platform and pipelines, set architecture and standards for data quality, governance, and observability, enable analytics and ML at scale, mentor engineers, and drive platform-wide performance and reliability improvements.

Lead delivery of enterprise data governance, MDM, and modern data-platform components including Snowflake, CDC pipelines, schema-first contracts (JSON Schema/Avro/Protobuf), data catalog and PII classification, CI/CD schema management, AWS data services, and day-to-day leadership of an offshore delivery team.

Design, build, and optimize scalable batch and streaming data pipelines. Model and process structured and unstructured data, ensure production reliability and observability, apply ML techniques for insight extraction, and drive data strategy while adopting modern tooling and IaC practices.

Design, build, and maintain large-scale data pipelines and ETL/ELT processes using Redshift, Azure Data Factory, Hadoop/Hive, Spark/Databricks, and Snowflake on cloud platforms (GCP). Implement Scala-based data processing, optimize performance, and support analytics and data warehousing needs.

Build and own data platform infrastructure and scalable pipelines (ingestion, orchestration, transformation, delivery). Define governance, observability, and AI-first architecture. Optimize performance, ensure data quality, mentor engineers, and integrate AI-assisted tooling.

Lead technical design and hands-on development of Pantheon’s data platform, extending ingestion, curation, retention services and ML/LLM infrastructure. Mentor engineers, drive architecture and engineering standards, operate in a DevOps model, partner cross-functionally, and participate in on-call support to ensure reliable, compliant data access and analytics.

Own and evolve the data platform: architect, build, and maintain data models, pipelines, and access layers. Ensure data quality, governance, and reliability; monitor and optimize infrastructure; provide technical leadership, code reviews, and collaborate with cross-functional teams to deliver scalable data solutions for operations and analytics.

Build and maintain scalable data pipelines and Python automations for ingesting, processing, and enriching data. Integrate SaaS sources via APIs, use Databricks/PySpark/Delta Lake and AI tools to power lead generation, enrichment, deduplication, and entity resolution. Collaborate cross-functionally to translate business needs into automated, production-ready solutions.

Design, build, and maintain scalable ETL/ELT pipelines and data models to populate the data warehouse. Write reusable SQL and Python to transform data, monitor pipeline reliability and quality, enable AI workflows, collaborate with stakeholders, and drive adoption of modern data stack technologies (DBT, Iceberg, Databricks). Support data governance, observability, and security practices.

Lead design and modernization of enterprise data pipelines across cloud and on-prem, define platform standards, mentor engineers, integrate utility systems (SAP, GIS, SCADA, AMI), ensure governed data for analytics and regulatory filings, and drive infrastructure-as-code, CI/CD, and observability for production pipelines.

The Senior Data Engineer will design and build scalable data platforms, handle automation for data operations, and ensure reliability and performance of data pipelines while collaborating with various teams.

Lead a team to design, build, and maintain cloud data engineering solutions using Microsoft Fabric and Azure Synapse. Responsibilities include architecture, data pipelines, lakehouse and warehousing implementation, governance, performance optimization, mentoring engineers, cross-functional collaboration, support/on-call, documentation, and ensuring reliability and compliance.

Build, operate, and modernize scalable data pipelines and warehouse environments for lottery reporting, analytics, and AI readiness. Improve ingestion, transformations, data models, contracts, observability, lineage, and onboarding patterns. Automate operations, troubleshoot production issues, mentor engineers, and support migration from legacy to cloud architectures while ensuring data quality, reliability, and regulatory continuity.

The Staff Data Engineer will lead financial data operations, ensuring accuracy and reliability of data pipelines, while collaborating with Finance, Product, and Engineering teams to enhance systems and decision-making processes.

Lead the vision and execution for scalable data governance and quality across Airbnb. Architect future-proof data ecosystems, define best practices and tooling, drive cross-functional policy implementation (privacy, legal, infosec), mentor senior engineers, and influence executive strategy to embed stewardship into the data product lifecycle.

Administer, stabilize, and scale Oura's global Databricks environment. Manage workspace governance, cluster policies, access control, cost monitoring, job scheduling, and incident SLAs. Onboard teams, resolve performance and reliability issues, and partner with IT and Data Architecture. Over time contribute to pipeline development, Spark optimization, CI/CD, and infrastructure-as-code to support domain-oriented data mesh initiatives.

Lead architecture and scaling of the Databricks data warehouse and self-service data platform. Build reusable batch and streaming frameworks, enforce governance, SLOs, lineage, observability, CI/CD, and multi-tenancy. Mentor engineers, run design reviews, and ship documented patterns to enable analytics, data science, and app teams to move quickly without blocking on platform tickets.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

Design, build, and maintain backend systems and data interactions for healthcare reporting. Develop APIs, data models, ETL pipelines, and interfaces; optimize performance, ensure data integrity and security, and support automated testing and CI/CD. Collaborate in Agile teams and reduce technical debt while enabling integrations with frontend and third-party systems.