Top Remote Data Engineer Jobs in Cincinnati, OH

Build and operate scalable Databricks-on-AWS data pipelines using PySpark, Delta Lake, and lakehouse patterns. Optimize performance, implement data quality, monitoring, alerting, and automated remediation, and deliver curated datasets for BI and analytics partners. Collaborate with stakeholders on architecture and design while applying secure software engineering, CI/CD, agile, and operational stability practices. The role also uses AI-assisted development tools and supports workforce data analytics.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Define and govern enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, and AI use cases. Establish standards for data quality, lineage, access, cataloging, governance, observability, and SLAs. Architect AI-enabled workflows, resolve complex architecture issues, influence roadmaps, and mentor engineers through hands-on technical leadership. The role requires 15+ years of software, data engineering, or architecture experience and expertise in large-scale data platforms and modeling.

Designs, develops, and maintains scalable cloud-based data solutions, ELT/ETL pipelines, data warehouses, curated datasets, and transformation logic using Snowflake and related tools. Optimizes SQL and warehouse performance, monitors workflows, supports data governance and quality initiatives, documents data flows, and delivers trusted datasets for Tableau reporting. Collaborates with business and technical stakeholders in Agile processes while ensuring secure handling of sensitive PHI data.

Leads the design and implementation of scalable data architectures, ETL pipelines, data warehouses, migrations, and cloud data solutions. Provides technical leadership through system analysis, requirements gathering, design documentation, code and architecture reviews, SDLC management, production deployments, and mentoring junior engineers. Develops integrations using multiple programming languages, ETL platforms, cloud services, APIs, and CI/CD tools.

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).

Manage data engineering and analytics initiatives spanning SQL analysis, Power BI dashboards, dimensional data modeling, cloud data warehouses, and ETL pipelines. Partner with business stakeholders to translate questions into actionable insights and communicate findings clearly. Apply AI-assisted tools and evaluate LLM-based analytics capabilities such as natural-language querying, automated insight generation, and anomaly detection.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Builds and maintains reliable, scalable data pipelines for enterprise analytics. Responsibilities include data integration, transformation, profiling, quality monitoring, metadata management, SQL development, data mapping, ETL/ELT, testing, documentation, and performance optimization. Collaborates with analytics, warehousing, database, technology, and business teams in agile environments to deliver fault-tolerant solutions across legacy, cloud, web, and packaged data sources.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Leads enterprise data engineering strategy and architecture, designing scalable cloud data platforms, pipelines, models, governance, and AI-assisted engineering solutions. Establishes technical standards, security, observability, and reusable frameworks; influences architecture decisions across teams without direct management. Mentors senior engineers, partners with business and technical stakeholders, and remains hands-on with cloud technologies, software architecture, data modeling, and platform design.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.

Designs, builds, and operates scalable batch and streaming data pipelines on Databricks for federal missions. Responsibilities include developing Spark and Delta Lake solutions, managing clusters and workflows, implementing Unity Catalog governance and security, optimizing ETL/ELT processes, integrating CI/CD, supporting machine learning and advanced analytics, monitoring data quality, and collaborating with technical teams and stakeholders.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Designs and maintains data warehouse infrastructure, semantic and dimensional models, metrics layers, dashboards, reporting, and secure data pipelines. Builds governed self-service analytics in Sigma, Power BI, and Databricks; defines KPIs and data standards; implements row-level security and Entra ID controls; supports data quality, observability, AI/ML use cases, and BI adoption. Collaborates with technical and business stakeholders in an Agile-Scrum environment.

Build and maintain production data ingestion pipelines, transformation models, warehouses, and schemas for PerfectServe’s data platform. Deploy reliable, tested pipelines using idempotency and data-quality checks, apply dimensional and medallion modeling, participate in design reviews, and document owned pipelines and runbooks. The role requires SQL, programming experience, dbt or similar transformation tools, cloud data warehouse experience, ELT orchestration, AWS knowledge, and a strong data-testing mindset.

Designs, builds, and maintains enterprise data pipelines, ETL processes, integrations, data infrastructure, and complex data models. The role supports analytics partners, develops scalable data warehouse solutions, tunes SQL and databases, troubleshoots production issues, and collaborates with architects, analysts, developers, and stakeholders. Responsibilities include end-to-end project delivery, data modeling, big data engineering, production support, agile planning, and mentoring team members.

Own end-to-end data workflows, including ingestion, storage, orchestration, transformation, validation, and delivery. Build ETL/ELT pipelines, manage cloud data platforms and transfers, optimize data models and queries, maintain data quality monitoring, troubleshoot production issues, and document data flows. Collaborate with technical and nontechnical teams to investigate discrepancies, support enterprise datasets, and apply secure data-handling practices.

Build and maintain production ETL/ELT pipelines, data models, and curated datasets for analytics and AI applications. Ingest data from databases, APIs, SaaS tools, and event streams; implement quality checks, monitoring, documentation, governance, and access controls. Collaborate with analysts, data scientists, and ML engineers on reporting, feature datasets, AI search, and RAG applications. Optimize query performance and pipeline costs while using Git, code reviews, and CI/CD for safe releases.

Build and operate event-driven data pipelines processing healthcare claims data for fraud, waste, and abuse detection. Operationalize validated fraud signals, maintain alert delivery and partner dashboards, monitor pipeline reliability, and collaborate with data architects, scientists, and investigative teams. The role requires production-scale pipeline engineering using AWS, Snowflake, Python, and SQL in a fully remote environment.

Own and evolve UJET’s data platform, including MySQL CDC replication to BigQuery, dbt transformations, Terraform infrastructure, pipelines, data quality, observability, and Looker models. Build reliable, analytics-ready datasets and trusted metrics for internal and customer-facing reporting. Partner with Analytics, Finance, Product, and Engineering to deliver scalable data solutions, improve platform reliability, optimize performance and costs, and establish development standards and guardrails.

Build and maintain production data pipelines and foundational datasets for Market Intelligence. Automate survey ingestion, cleaning, joining, scheduling, monitoring, and reporting-layer integration. Develop repeatable queries, improve data quality practices, document assumptions, support reproducible outputs, and translate research needs into technical solutions. The role requires close collaboration with technical and nontechnical stakeholders and ownership of reliable data infrastructure.