Top Data Engineer Jobs in Fayetteville, AR

Designs, administers, and optimizes data systems supporting FSA IAM applications. Develops migration plans from legacy AIMS and PAS systems, creates data models, mappings, layouts, dictionaries, and integration processes, and supports analytics and fraud detection. Manages database configuration, performance tuning, monitoring, backups, capacity, secure data handling, encryption, network data flows, token management, and documentation while collaborating with development, security, and operations teams.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Manage, design, and optimize data activities for federal IAM systems. Develop data models, file layouts, source-to-target mappings, data dictionaries, integrations, ETL processes, and analytics support for identity analysis and fraud detection. Administer and tune databases, including configuration, backups, monitoring, capacity planning, and performance optimization. Support secure handling of identity and PII data, network-related data flows, documentation, token management processes, and collaboration across development, security, and operations teams.

Designs, builds, and operates scalable enterprise data platforms supporting ingestion, processing, storage, governance, analytics, and AI/ML. Responsibilities include developing batch and real-time pipelines, lakehouse and warehouse architectures, orchestration, infrastructure automation, CI/CD, monitoring, security, cost optimization, and observability. The role partners with data, ML, DevOps, security, and application teams, troubleshoots complex platform issues, establishes reusable engineering standards, contributes to architecture strategy, and mentors engineers.

Build and operate core data infrastructure for edge and hybrid environments, including ingestion, OT/IoT integrations, real-time streaming, data contracts, schemas, validation, lineage, observability, telemetry, and scalable query access. The role requires production ownership, reliability practices, customer integration, and collaboration across engineering and customer-facing teams. Candidates should have 6+ years of experience with production data, streaming, or integration systems and proficiency in Python and/or Go.

Lead the architecture and scaling of Overstory’s data platform for AI-powered vegetation analysis. Design and operate large-scale geospatial and temporal data pipelines, orchestration systems, event-driven workflows, service-oriented architecture, data quality frameworks, and observability practices. Drive architectural standards, system design, scalability, reliability, and traceability while mentoring engineers and collaborating with data, ML, and product teams.

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

Build and extend automated enterprise data governance and quality frameworks. Define measurable trust rules, data contracts, validation and certification workflows, metadata, lineage, tagging, and trust scoring across a cloud data platform. Integrate governance checks into developer workflows and Collibra, coach engineering and product teams, and establish organization-wide data trust standards. The role requires strong SQL, BigQuery or comparable warehouse experience, and expertise with data quality or observability tools.

Maintain and monitor Snowflake data warehouse and ingestion pipelines. Troubleshoot file/load/data quality issues, operate SFTP/drop-zone/file automation, support scheduled loads, build dbt models and tables, document runbooks, and serve as backup for core warehouse operations while collaborating with engineering and IT.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Develop reliable datasets supporting reporting, analysis, finance, revenue recognition, and company decision-making. Collaborate with Finance, Engineering, Sales, Product, and Marketing to understand data needs, improve manual workflows, validate data accuracy, and create scalable solutions using modern data tools.

Own end-to-end data platform architecture across ingestion, transformation, modeling, and serving. Improve warehouse efficiency, access control, reliability, observability, orchestration, CI/CD, and developer experience. Lead cross-functional technical initiatives, build monitoring and alerting, mentor engineers, guide architectural decisions, and develop AI-powered data tooling. The role requires strong SQL and Python skills, production experience with Airflow, BigQuery, and containerized infrastructure, plus architectural ownership and stakeholder communication.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

The Data Engineer designs, builds, and optimizes enterprise data pipelines, lakes, warehouses, integrations, and analytics infrastructure. Responsibilities include ELT development using Snowflake and Informatica, data governance, quality, security, performance monitoring, PII protection, cost optimization, and support for analysts and data scientists. The role also delivers process improvements, analytics tools, stakeholder collaboration, technical mentoring, and scalable data architecture across cross-functional teams.

The ClaimsXM Data Engineer III role has limited responsibilities listed. The position requires U.S. citizenship or naturalized citizenship and requires all work to be completed within the continental United States, Alaska, or Hawaii.

Leads the design, development, administration, and optimization of enterprise data pipelines and Microsoft Fabric data platforms. Responsibilities include ETL/ELT architecture, medallion layers, data quality, CI/CD, infrastructure as code, security, hybrid connectivity, disaster recovery, performance optimization, and troubleshooting. The role also mentors engineers, establishes technical standards, supports architecture reviews, and provides senior-level guidance for secure, scalable, reliable data platform implementations.

Lead design and development of web UI and REST middleware within an Agile team. Define requirements and acceptance criteria, implement Java/Spring and Angular solutions, build CI/CD pipelines, work with Product Owner and testers, and produce/update design documentation.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.

The DMS Data Engineer is responsible for analyzing raw data, building data systems and pipelines, conducting complex data analyses, and collaborating on projects to enhance data quality and reliability.

Designs, builds, and maintains scalable batch and streaming data pipelines, ETL/ELT workflows, data models, and Python transformations. Writes optimized SQL, monitors pipeline reliability, troubleshoots failures, implements data-quality validation, and supports security and governance practices. Collaborates with engineers, analysts, stakeholders, and clients to define data contracts, improve platforms, document lineage, and deliver reliable analytics data.

Designs and optimizes ETL pipelines supporting analytics, data science, and machine learning. Builds labeling, retraining, MLOps, model monitoring, and production inference workflows. Contributes to lakehouse architecture, data modeling, governance, lineage, and quality. Uses infrastructure as code and containerization to create reproducible environments, collaborates with data scientists and BI teams, participates in code reviews, and improves engineering practices.

Design, build, maintain, and optimize Spark and Scala ETL/ELT pipelines on AWS for terabyte- and petabyte-scale datasets. Work across S3, Snowflake, Kafka, and monitoring systems, resolve production data-quality and performance issues, write automated tests, participate in architecture and code reviews, and support on-call operations. Use generative AI to improve pipeline development, monitoring, and engineering productivity.

Build and scale the data backbone for HR, payroll, finance, and insurance products. Design data models across warehouses and data lakes, develop and optimize batch and real-time ETL/ELT pipelines, improve data quality and governance, tune SQL queries, address data integration gaps, and collaborate with engineering, BI, analytics, and business stakeholders.