Top Data Engineer Jobs in Cleveland, OH

Own and optimize data infrastructure and ETL/ELT pipelines using SSIS and Microsoft Fabric. Build and maintain SQL Server data warehouse staging, transformations, and lakehouse ingestion. Implement CI/CD, version control with GitHub, monitor pipeline health, troubleshoot data issues, document data flows, and translate stakeholder requirements into data engineering solutions that support BI reporting.

The Data Engineer will design and maintain data pipelines, optimize cloud data warehouses, and collaborate with analytics teams while mentoring junior staff.

Build and maintain production-grade data pipelines and ELT processes using Python, SQL, Airflow, and dbt within AWS. Design optimized schemas in PostgreSQL and Redshift, leverage AI/LLMs to improve development, automation, testing, and data quality, and scale infrastructure for large healthcare datasets.

Build and maintain AWS-based ETL pipelines (Glue/PySpark, MWAA/Airflow, Lambda) to ingest and process terabytes of financial data into an S3 data lake using Iceberg/Parquet. Implement metadata, monitoring, tests (90% coverage), materialized views (Trino/Athena), CloudFormation deployments, documentation, and support agile delivery and operations for federal clients.

Lead telemetry and experimentation data engineering for Global Skilling products. Own instrumentation and telemetry contracts, build A/B testing pipelines and semantic models, integrate catalog/content data, deliver external reporting, operate insights feedback loops, mentor mid-level engineers, and provide on-call leadership for product-tier pipelines.

Build and maintain scalable batch and streaming data pipelines on GCP, develop API-driven integrations, design data models, ensure data quality and performance, and deliver end-to-end solutions in a client-facing healthcare data environment.

Design, build, and maintain Scala-based ETL and streaming data pipelines, APIs, and data microservices in Azure. Optimize LLM fine-tuning and RAG workflows, support DevOps/CI-CD practices, document standards, mentor engineers, and manage data ingestion frameworks for analytics and partner integrations.

Design and implement scalable cloud data platforms and end-to-end ETL/ELT pipelines, migrate legacy infrastructure to Snowflake/AWS, optimize data warehouses, build transformations with dbt and Dagster orchestration, develop data processing with Python and SQL, implement IaC and CI/CD, and ensure data observability and governance while collaborating with stakeholders.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.

Serve as the technical owner for customer-facing data workflows and integrations: build pipelines, write SQL and Python scripts, deploy AI-powered workflows, manage APIs and Snowflake ingestion, and translate repeatable solutions into product requirements while supporting customer success and internal engineering.

Design, build, and maintain enterprise data warehouses and lakes; implement ETL pipelines; optimize data models and performance; produce analytics and predictive models; create dashboards and visualizations; monitor data quality; document and collaborate with stakeholders to drive data-driven business solutions.

Design, build, and maintain cloud and on-prem data warehouses, data pipelines, and ETL/ELT workflows (Snowflake, SQL Server, Airflow, DBT, Python). Ensure data quality, performance, CI/CD, and support analytics-ready datasets for reporting and BI while collaborating with stakeholders and following Agile practices.

Designs, builds, and maintains scalable data warehouses, pipelines, and lakes using the Microsoft analytics stack. Transforms and models data, develops ETL/ELT, reporting, and dashboards, and partners with clients to gather requirements and deliver reliable analytics solutions. Works across the full delivery lifecycle and within Agile practices.

Design, build, and maintain scalable ETL/ELT data pipelines and analytical platforms on AWS and Databricks. Migrate legacy workflows to cloud-native services, implement data lake and catalog solutions, enable AI/RAG/vector search, ensure FedRAMP/NIST compliance, optimize performance, and mentor junior engineers while collaborating in Agile teams.

Lead modernization of the enterprise data platform and build scalable ETL/ELT pipelines using SQL, Python, Talend, and Databricks. Drive migration from DB2/SQL Server to Databricks, set data quality, observability, and lineage standards, design data models, contribute to CI/CD and IaC practices, and mentor junior engineers while partnering with analytics, product, and business stakeholders.

Design, build, and operate Alpaca's scalable data platform and lakehouse infrastructure. Build low-latency streaming and CDC ingestion, batch pipelines landing in Iceberg, and tooling for BI and AI consumers. Manage IaC, Kubernetes deployments, monitoring/incident response, and partner with DevOps and analytics teams to meet growing data requirements.

Own end-to-end client data migrations: reverse-engineer legacy ETL, design and build Airflow DAGs and Spark jobs, validate and reconcile data at scale, document architecture and decisions, and coordinate cross-functional teams through go-live and hypercare.

Design, build, and own end-to-end data systems and pipelines powering payments, fraud response, and reporting. Implement streaming and batch integrations, APIs, and workflow orchestration. Define data correctness with monitoring, alerting, and tests. Create reusable patterns and collaborate with stakeholders to clarify requirements and tradeoffs.

The Data Architect will design scalable data models, transform raw data into actionable insights, and deploy analytics solutions for healthcare providers, with a strong emphasis on SQL proficiency and cloud technologies.

Design and scale data infrastructure, ensure data quality and governance, collaborate with teams on data products, and modernize practices.

Design and maintain data systems for business-critical insights, connect data across platforms, build ETLs, and support business data decisions.

The Senior Data Analytics Engineer will develop data pipelines, support data-driven decision-making, collaborate with stakeholders, and improve data quality and processes.

The Data Engineer builds and maintains data infrastructure, develops data pipelines, ensures data quality, and collaborates with teams for insights.

The Senior Data Engineer will develop data engineering solutions, manage ETL workflows, and establish a new data practice while collaborating with stakeholders.

Lead design, build, and support of cloud-based ETL, data warehousing, and reporting solutions. Develop/optimize ETL pipelines, data models, and queries; deploy containerized services on Kubernetes; automate workflows; mentor teammates; collaborate with architects, data scientists, and stakeholders to deliver scalable analytics and reporting products.