Top Data Engineer Jobs in Cincinnati, OH

Senior Data Engineer on PwC's Managed Data, Analytics & Insights team to design, build and manage advanced data ecosystems. Responsibilities include designing data solutions and scalable pipelines, solving complex problems, mentoring junior staff, maintaining high delivery standards, and building client relationships while aligning solutions to business context.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

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.

Build and maintain reliable data pipelines, Airflow DAGs, and Snowflake-based Data Vault/warehouse models. Implement CI/CD, automated testing, observability, and production support while partnering with stakeholders and developing insurance domain expertise.

As a Data Engineer II, you will build, scale, and optimize data platforms, focusing on ETL/ELT pipelines, and collaborate with data scientists and AI engineers to support data-driven applications and insights.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

As a Manager in Managed Services, you'll lead data ecosystems projects, mentor juniors, manage client accounts, and deliver data strategies to optimize operations and drive insights for clients.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

As a Data Engineer at Edmunds, you will architect and maintain data platforms for analytics and AI/ML, ensuring scalable, reliable data processing and supporting cross-functional collaboration.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

Maintain data integrity and quality through advanced testing and validation of ETL pipelines. Analyze complex data issues, build solutions, mentor junior staff, engage with clients, and support continuous improvement across data management, governance, and pipeline orchestration.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

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 support scalable data platforms and pipelines (ETL, warehousing, APIs) using DBT, Snowflake, and SQL. Collaborate with architecture, security, and application teams, build CI/CD, provide escalated on-call support, mentor staff, and deliver data solutions for the bank.

Design, develop, and maintain Power BI BI solutions: build data models, create reports and dashboards, write DAX and Power Query transformations, query and integrate data with SQL, apply data warehousing/ETL principles, optimize performance, document solutions, troubleshoot issues, and collaborate with stakeholders.

Lead client discovery and design scalable Lakehouse/warehouse analytics platforms on Azure and Microsoft Fabric. Implement ingestion, transformation, orchestration (SQL, Spark, KQL), build analytics-ready models, enable compute engines, design AI-enabled agentic solutions, enforce governance/security, establish monitoring/runbooks, and produce consulting deliverables and enablement for handoff.

Design, build, and maintain ETL pipelines and data infrastructure to support analytics and reporting. Write and schedule workflows (Airflow DAGs), manage third-party data ingestion, implement data validation, optimize performance, and collaborate on data models, governance, and ML model deployments.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

Lead architecture and implementation of scalable data pipelines, integrations, and cloud data infrastructure. Own IaC (Terraform), workflow orchestration (near-real-time and batch), third-party API and AI/LLM integrations, data export pipelines, and internal tooling. Mentor engineers, drive technical strategy and cross-team priorities, and communicate tradeoffs to technical and non-technical stakeholders. Must work EST/CST hours and be authorized to work in the US.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Develop and maintain Python-based data applications and pipelines: fetch data from databases/APIs/files, build Pandas DataFrames/NumPy arrays, write complex SQL and data transformations, handle nested JSON, and collaborate with data science teams to deliver production data platforms.

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.

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.