Top Data Engineer Jobs in Baltimore, MD

The Forward Deployed Data Engineer at ZoomInfo designs and delivers custom intelligence applications for strategic accounts, managing engagements end-to-end and collaborating with various teams to optimize data solutions.

Design, build, and maintain scalable batch and near-real-time data pipelines and data products using Databricks and Spark. Integrate internal/external sources, implement data models, ensure data quality, support BI (Sisense), CI/CD, and collaborate with analysts, scientists, and stakeholders to enable analytics and decision-making.

As a Data Engineer, you will build and maintain data pipelines, convert data into metrics, and operate the platform on AWS. Responsibilities include writing production code, processing data from APIs, and enhancing the orchestration layer using Dagster, while ensuring compliance in a HIPAA-regulated environment.

The Data Engineer will design and maintain data pipelines, enhance the data warehouse, support machine learning applications, and collaborate with various teams to leverage data effectively.

The Data Engineer I at Storable supports the design and maintenance of data pipelines, works with ETL processes, optimizes workflows, and collaborates with various teams to enhance data accessibility and reliability.

As a Data Engineer, you will build and maintain data pipelines, ensure data quality, support ML workflows, and optimize query performance using various technologies.

As a Senior Forward Deployed Data Engineer, you will design and implement data pipelines for media agency clients, manage technical delivery, and ensure data security while mentoring junior engineers and communicating effectively with stakeholders.

The Looker Data Engineer will build and optimize data models, develop reporting solutions, and support data-driven decisions using Looker and other technologies.

The role involves designing and optimizing data pipelines in Snowflake, developing ETL workflows, collaborating on data integration, ensuring data security, and performance monitoring.

As a Data Engineer, you will design and maintain data pipelines, support DoD systems, and ensure data quality and compliance.

The Data Engineer is responsible for designing data workflows and pipelines, implementing systems engineering principles, and deploying data streaming technologies while ensuring compliance with industry regulations and best practices.

Forward-deployed senior data engineer who deploys, hardens, integrates, and operates a data platform (lakehouse, streaming, governance) in DoD environments. Build pipelines across classification boundaries, produce accreditation evidence (STIG/cATO/FIPS), run day-2 operations, train mission teams, and mentor junior FDEs while feeding field feedback into product improvements.

Design, build, and maintain scalable ELT data pipelines and models to power reporting, analytics, and operational workflows. Troubleshoot data quality and performance, participate in incident response, support CI/CD deployments, collaborate with cross-functional teams, and contribute to data platform modernization and engineering best practices.

Design, implement, and maintain scalable data pipelines and platforms for structured and unstructured data to support AI/ML workloads. Ensure data governance, security, monitoring, and CI/CD for ETL workflows across on‑prem, cloud, or hybrid environments while collaborating with cross‑functional technical teams.

The Forward Deployed Data Engineer will manage AI-automated data platform migration projects, ensuring timely delivery and communication with clients while utilizing proprietary tools.

The Data Engineer will build and maintain scalable data pipelines, ensure data quality, and collaborate with teams to meet data requirements for federal government projects.

Design, build, and maintain enterprise-scale data platforms and scalable ETL/ELT pipelines. Develop and operationalize data transformations in Palantir Foundry, debug logs, troubleshoot issues, and convert varied data into usable formats. Guide cross-functional teams, support platform scaling, monitoring, and operations, and apply PySpark/Spark SQL for analytics and reporting to support mission-driven use cases.

Design, implement, and administer operational data flows: ingest, clean, enrich, transform, secure, and move mission-critical data. Perform exploratory data analysis, troubleshooting, and collaborate with customers to ensure compliant, reliable data pipelines.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

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.

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.

Design, implement, and maintain automated ingestion and transformation workflows to bring spreadsheet-based operational data (JSON/CSV/Excel) into enterprise databases and Pega workflows. Validate data integrity, troubleshoot pipelines, support reporting integrations, document data flows, and collaborate with architects, developers, and stakeholders to modernize and secure enterprise data services.

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.

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).