Top Data Engineer Jobs in Little Rock, AR

Own product telemetry, maintain dbt models in Snowflake, support self-service reporting and NLP queries, drive product insights from user and retention data, and monitor Airbyte syncs to ensure data quality.

Sets technical direction and architectural coherence across Netflix’s Ads Data Engineering organization. The role guides cross-domain data system decisions, partners with engineering and business leaders, mentors data engineers, shapes platform-wide initiatives, and advances AI-native data engineering. Responsibilities span modern data foundations, ad-tech data flows, privacy and governance, platform migrations, scalable data products, and collaboration with Ads Platform Engineering, Data Science, and Product teams.

Build and operate ingestion and transformation pipelines to turn messy insurance data into clean datasets. Design bronze/silver/fact layers, model and tune Postgres and analytical stores, define KPIs and reconciliation checks, orchestrate observable data jobs, and ensure end-to-end data quality for analytics and AI products.

Lead architecture, systems, and roadmap for Coinbase's Compliance Reporting Data Platform. Design and build production-grade data models, pipelines, quality frameworks, validations, and certification processes. Establish data contracts, monitoring, reconciliation, and audit-ready processes. Partner with engineering and compliance stakeholders, respond to regulatory exams, and mentor engineers while shaping standards and documentation.

As a Data Engineer, you will build and maintain data pipelines, convert data into metrics, and operate the platform on AWS. Responsibilities include writing production code, processing data from APIs, and enhancing the orchestration layer using Dagster, while ensuring compliance in a HIPAA-regulated environment.

As a Data Engineer, you will build and maintain data pipelines, ensure data quality, support ML workflows, and optimize query performance using various technologies.

The Data Engineer is responsible for designing data workflows and pipelines, implementing systems engineering principles, and deploying data streaming technologies while ensuring compliance with industry regulations and best practices.

Forward-deployed senior data engineer who deploys, hardens, integrates, and operates a data platform (lakehouse, streaming, governance) in DoD environments. Build pipelines across classification boundaries, produce accreditation evidence (STIG/cATO/FIPS), run day-2 operations, train mission teams, and mentor junior FDEs while feeding field feedback into product improvements.

Design, build, and lead a scalable, configuration-driven data integration platform. Improve performance, automate monitoring and deployments, provide escalated support, and collaborate across product and engineering to deliver reliable, secure data integrations.

Design and maintain scalable ETL/ELT pipelines, analytical dashboards, and reporting tools supporting clinical, research, and operational priorities. Write production-quality Python, R, and SQL; develop data architecture; partner with data scientists to operationalize analytics; integrate AI-enabled workflow improvements; establish engineering standards; and mentor junior team members.

Owns analytics datasets and data products for Netflix’s cloud games infrastructure. Builds scalable pipelines, data models, metrics, dashboards, tooling, and self-service assets from massive telemetry data. Partners with infrastructure engineering, data engineering, and data science teams to improve quality of experience, capacity, and utilization insights. Responsibilities include telemetry and logging, visualization, trustworthy documentation, scalable data solutions, and independently prioritizing high-impact investments.

Design, develop, and maintain scalable data pipelines and models in Databricks and Microsoft Azure. Perform data ingestion, transformation, integration, migration, and modernization across enterprise data sources. Monitor and optimize pipeline performance, reliability, and data quality. Translate business requirements into technical solutions, collaborate with architects and engineers, follow security and governance standards, participate in code reviews, and document data platforms and processes.

Support building and maintaining ETL pipelines using Python and PySpark. Work with AWS services (Glue, Lambda, Step Functions, S3, Redshift, RDS, EMR) to ingest, transform, and validate data. Write complex SQL, implement basic monitoring/logging, process API/JSON payloads, and collaborate with engineers, analysts, and stakeholders on deployment and documentation.

Design and implement scalable data pipelines and orchestration on GCP to support AI/ML-driven mail intelligence. Build stream and batch processing, ensure data governance and quality, optimize workflows, create visualizations, and collaborate with US cross-functional teams to align technical strategy and recover from failures.

Design, develop, maintain, and optimize ADABAS database structures and data solutions. Support mainframe data processing (Natural, COBOL, JCL), perform migrations to modern platforms, tune performance, ensure data integrity/security, troubleshoot issues, and collaborate with developers, architects, and analysts on modernization, API/cloud integration, and data warehousing initiatives.

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, quality frameworks, governance, observability, and self-service analytics practices. Partners with stakeholders, leads technical reviews, mentors engineers, supports hiring, oversees analyses, and participates in incident response and continuous improvement.

Design, build, and maintain data lakes, data warehouses, and end-to-end data pipelines for a multi-tenant SaaS platform. Lead code and design reviews, write infrastructure-as-code, optimize performance and scalability, participate in data governance, mentor junior engineers, evaluate new technologies, automate tests, collaborate cross-functionally, and join on-call rotation to ensure reliable data delivery.

The description combines recruitment management, event planning, business development consulting, and iOS application development. Responsibilities include developing recruitment strategies, training recruiters, maintaining candidate databases, coordinating events, managing vendors and client relationships, developing sales pipelines, and designing, testing, updating, and maintaining iOS applications.

The description is inconsistent with the title and primarily covers recruitment management, event planning, business development, and iOS application development. Responsibilities include developing recruitment strategies, managing recruiters and candidate databases, coordinating events and vendors, creating business development plans, managing client relationships, and designing, testing, and maintaining iOS applications.

Designs and maintains pipelines migrating financial data from legacy Oracle databases to Oracle ERP. Develops SQL and PL/SQL transformations, backend integrations, data mappings, reconciliation, testing, monitoring, troubleshooting, and documentation. Partners with accounting, business, and technical stakeholders to resolve issues and support large-scale financial transformation. The role also involves AI tool adoption, code reviews, solution design, deployment activities, governance, communication, mentoring, and project oversight.

Own the architecture, technical direction, reliability, and evolution of a shared cloud data platform. Lead hands-on development of streaming and batch pipelines using infrastructure-as-code, build governed analytical data products, and establish data contracts, quality, security, lineage, and observability standards. Serve as the final technical escalation point, mentor engineers, lead design and code reviews, promote responsible AI-assisted coding, respond to incidents, and communicate technical tradeoffs across stakeholders.

Design and maintain ELT/ETL pipelines and cloud data warehouse models, build Power BI reports, apply AI tools to accelerate data workflows, and partner with business stakeholders to deliver reliable analytics and data governance.

Lead the design, development, optimization, and maintenance of scalable batch and streaming data pipelines. Build ETL/ELT workflows using Python, SQL, and AWS services; optimize data models and cloud data platforms; ensure data quality, integrity, reliability, and performance; establish CI/CD and infrastructure-as-code practices; resolve pipeline issues; document systems; and mentor junior engineers.

Own end-to-end data engineering for a business domain, including API and other-source ingestion, dbt models, orchestration, data-quality testing, monitoring, documentation, and performance. Build reliable, AI-ready single sources of truth and ensure data freshness, accuracy, traceability, and scalability. Collaborate with stakeholders and the Director of Data, establish engineering patterns, review work, and mentor other engineers.

Design, implement, and optimize Snowflake data storage and ETL solutions. Write and tune SQL, integrate data sources and BI tools, ensure data security and compliance, and collaborate with teams to meet business objectives.