Top Data Engineer Jobs in Columbus, OH

Develop and maintain secure, scalable data pipelines and production code using AWS, PySpark, Python, and ETL technologies. Extract and transform data, implement quality checks, optimize data processing workflows, support cloud data modernization, and ensure reliable data availability. Collaborate with cross-functional teams, troubleshoot technical issues, automate recurring remediation, and communicate with technical and non-technical stakeholders. The role also involves software testing, operational stability, continuous delivery, and potentially mentoring other engineers.

Leads complex data engineering initiatives for global cybersecurity analytics. Designs data architectures, ingestion pipelines, governance standards, and quality processes; collects and transforms large datasets; develops Power BI visualizations; improves data reliability and accessibility; documents systems; advises stakeholders; mentors team members; and responsibly applies AI tools to improve workflows while protecting confidential data.

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.

Build and operate production data pipelines and dimensional models using Spark, SparkSQL, and cloud lakehouse technologies. Own pipelines from requirements through deployment, monitoring, and iteration; improve data quality, lineage, reliability, and cost efficiency. Partner with data scientists, analysts, product managers, and engineers to support datamarts, KPIs, reporting, and analysis. Participate in business-hours on-call rotations and improve runbooks and alerting.

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.

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).

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.

Build and operate platform infrastructure for product analytics, data processing, and ML systems. Responsibilities include event tracking pipelines, Databricks Bronze-to-Gold lakehouse layers, schema evolution, data contracts, CI validation, backfills, monitoring, feature computation, model pipelines, lineage, PHI policy enforcement, and self-service developer tooling. The role focuses on making data and ML changes safe, observable, reproducible, and accessible to engineers and subject-matter experts.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

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.

Build and maintain data pipelines, governance documentation, and data architectures supporting Department of State systems. Collect, clean, transform, integrate, validate, and analyze data from multiple sources using Palantir Foundry and relational databases. Collaborate with nontechnical stakeholders to gather requirements, identify data-quality issues, develop reporting tools and visualizations, and recommend improvements supporting data-driven decisions.

Build and optimize scalable ETL pipelines, monitoring and alerting systems, and reliable data solutions for legal and eDiscovery infrastructure. Responsibilities include query and workflow optimization, cross-functional requirements gathering, system documentation, deployment planning, and compliance with data privacy and security standards. The role also contributes to internal consulting, thought leadership, and practice development.

Design, build, and optimize complex ETL pipelines, data workflows, monitoring, and alerting systems supporting legal and eDiscovery infrastructure. Lead end-to-end data engineering initiatives, improve query and pipeline performance, document deployment strategies, and ensure privacy, security, and compliance. Collaborate with attorneys, data scientists, engineers, and other stakeholders in regulated environments while contributing to internal consulting and thought leadership initiatives.

Designs and builds cloud-based, data-centric applications supporting clinical and operational healthcare processes. Develops data pipelines, transformations, enrichment processes, provisioning layers, and user interfaces. Collaborates with Product, Platform, and Architecture teams using modern software development practices, source control, documentation, and regular delivery methods.

Designs, builds, tests, deploys, and maintains secure ETL/ELT pipelines for public health data. Integrates Power Platform, Azure services, APIs, SQL Server, CDC/NIOSH systems, and approved file-transfer platforms. Develops batch, event-driven, and near-real-time data workflows supporting intake, validation, reporting, analytics, notifications, and results delivery while meeting FISMA Moderate, federal privacy, Agile, and DevSecOps requirements.

Designs and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, and SQL. Supports SQL Server, SSIS, SSRS, Azure, data warehousing, reporting, analytics, and AI/ML workloads. Responsibilities include optimizing performance and reliability, implementing data quality controls, integrating enterprise data sources, modernizing platforms, and partnering with business and IT teams to deliver scalable data solutions.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

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

Designs, builds, and maintains scalable batch and streaming data pipelines in Databricks using Python, Spark, SQL, and Delta Lake. Supports SQL Server, SSIS, and SSRS environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, applies warehousing best practices, and partners with business and IT teams to deliver reporting, analytics, and AI/ML data solutions.

Lead the architecture and development of Vercel’s next-generation data platform, supporting batch and real-time integrations, analytics, data warehousing, and AI/ML workloads. Design scalable systems using Kafka, ClickHouse, Tinybird, and Snowflake; establish data governance and security standards; guide architectural decisions and roadmaps; collaborate with engineering, product, security, compliance, and leadership teams; write production code; and mentor engineers.

Build and maintain scalable ETL/ELT pipelines, integrate travel and customer data from internal and external systems, develop cloud data warehouse models, implement data quality monitoring, support real-time streaming, and ensure data privacy and regulatory compliance. Collaborate with analysts, data scientists, and product teams to deliver reliable datasets for personalization, forecasting, pricing, route optimization, and customer experience initiatives.

Design, build, and maintain scalable data pipelines, data models, and analytics solutions using Microsoft Fabric and Azure. Develop Lakehouse and Warehouse architectures, Fabric Notebooks with PySpark and Spark SQL, ETL/ELT workflows, and Power BI semantic models. Optimize performance, ensure data quality, security, governance, and regulatory compliance, and collaborate with analysts, data scientists, and business stakeholders to support business intelligence and advanced analytics.

Build and maintain Databricks data pipelines across bronze, silver, and gold layers. Ingest APIs, logs, billing exports, and reference data; implement attribution logic, governance, data quality monitoring, and cost optimization. Manage Unity Catalog permissions, lineage, refresh schedules, incremental processing, and CI/CD workflows while supporting multi-cloud storage and high-volume caller-identity data.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.