Top Data Engineer Jobs in Oklahoma City, OK

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Design and implement data architecture and scalable ETL/ELT pipelines on cloud platforms (AWS/Azure/GCP). Analyze complex problems, mentor junior staff, ensure data governance/security compliance, liaise with stakeholders, and deliver high-quality client solutions while developing technical and client-facing skills.

Design and build data infrastructure and pipelines using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, collaborate with clients, and apply data architecture, transformation, and analytics to deliver insights.

Develop and maintain data pipelines and ETL processes, optimize SQL and data architectures, build BI reports and dashboards (Oracle BI, QlikView, Spotfire), analyze data to produce strategic insights, support client engagements in regulated environments, and apply continuous process improvements to ensure data quality and integrity.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

The Data Engineer will develop and implement ETL processes, design data architectures, work with data platforms, and ensure data solutions meet scalability and security needs. Responsibilities include mentoring junior engineers and collaborating with stakeholders to gather requirements and optimize data transformations.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

Lead development and delivery of data engineering solutions for clients: design data architectures, build and maintain ETL pipelines, apply advanced analytics, implement BI visualizations, coach teams, manage managed-services SLAs, and engage stakeholders to validate and refine outcomes.

Lead data engineering engagements delivering ETL/ELT solutions, pipelines, and cloud analytics. Build and maintain architectures (DataStage, AWS/Redshift, DB2/SQL Server, GoldenGate/CDC, S3/Glue), oversee BI and visualization, manage client accounts and teams, mentor junior staff, ensure project delivery and quality, and drive process and technology adoption.

Support the design, build, and maintenance of scalable data pipelines and data management systems using cloud platforms. Assist in ETL, data transformation, reporting, and dashboard creation (Power BI/Tableau). Troubleshoot pipelines, help integrate new technologies (including AI/ML tools), ensure data security/compliance, and communicate with finance/accounting stakeholders across multiple client engagements.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

Design, build, and maintain batch and near-real-time data pipelines on Microsoft Fabric and Azure. Ingest, transform, and curate raw source data into analytics-ready tables, semantic models, and Power BI datasets. Ensure data governance, security, observability, and cost-efficient ELT/ETL workflows. Troubleshoot data quality and pipeline issues, document architecture and lineage, and collaborate with stakeholders to deliver reliable data products.

Build, maintain, and test data pipelines and ETL/ELT processes using Python and SQL; integrate Kafka streams; model data in Snowflake; implement CI/CD (GitHub Actions), logging, observability, and on-call support. Collaborate with senior engineers and application teams to enable self-service data ingestion and improve pipeline reliability and data preview capabilities.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform 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.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.

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.

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.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

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

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).