Top Data Engineer Jobs in Baltimore, MD

Lead and manage managed data and analytics engagements, build modern data ecosystems (Databricks/Kafka), convert insights to strategy, mentor teams, oversee project delivery, quality, risk, and client relationships.

Design and implement data architecture and scalable ETL/ELT pipelines, integrate data across cloud platforms (AWS/Azure/GCP), mentor junior staff, ensure data governance and security, build client relationships, and deliver high-quality data solutions that inform business decisions.

Design, implement, and manage advanced data ecosystems and scalable ETL/ELT pipelines. Analyze complex problems, deliver data warehousing and governance solutions, mentor junior staff, and build client relationships while upholding professional standards.

Design, build, and maintain data infrastructure, pipelines, lakes/warehouses, and ETL/ELT workflows. Use cloud platforms and tools (AWS, Azure Data Factory, Databricks, Airflow, Hadoop) to transform and validate data, ensure quality and security, collaborate with clients, and support data strategy and analytics to drive business decisions.

Lead design and delivery of data architecture, pipelines, warehouses and lakes across cloud platforms. Manage and mentor teams, ensure data governance/security, implement ETL/ELT, IaC and DevOps practices, and translate stakeholder requirements into scalable data solutions to support analytics and decision-making.

Design, build, and maintain data pipelines and ETL processes, optimize SQL, and produce BI reports and dashboards using tools like Oracle BI, QlikView, and Spotfire. Collaborate with teams to analyze data, apply data architecture principles (star/snowflake schemas), ensure data quality, and support client engagements in regulated environments while continuously improving processes and technical skills.

Lead design and implementation of data architecture and cloud-based data engineering solutions. Manage large projects, develop data models and pipelines, ensure governance and security, advise clients strategically, and build high-performing inclusive teams to drive project success.

Design and implement data architecture and scalable ETL/ELT pipelines using cloud platforms (AWS/Azure/GCP). Develop data models, ensure governance and security compliance, analyze complex problems, mentor junior staff, build client relationships, and enhance technical skills to deliver high-quality data solutions.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop and manage data lakes and warehouses, apply data architecture and dimensional modeling, validate data quality, implement security best practices, and collaborate with clients to deliver analytics and data strategies. Mentor peers and manage growing client responsibilities as a Senior Associate.

Lead design and delivery of cloud-based data architecture, data models, ETL/ELT pipelines, data warehouses/lakes, and data security. Manage projects, mentor team members, ensure governance compliance, collaborate with stakeholders, and optimize cloud infrastructure for performance, cost, and scalability.

Lead design and implementation of data architecture and cloud-based data engineering solutions. Manage large projects, translate stakeholder requirements into technical designs, ensure data governance and security, develop data models and pipelines, advise clients strategically, and coach teams to deliver operational excellence.

The Data Engineer will develop and implement ETL processes, design data architectures, work with data platforms, and ensure data solutions meet scalability and security needs. Responsibilities include mentoring junior engineers and collaborating with stakeholders to gather requirements and optimize data transformations.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

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.

Lead design and delivery of data engineering solutions for clients: build and maintain data pipelines and architectures, leverage AWS/Redshift, ETL and BI tools, apply systems thinking, drive analytics insights, manage/manufacture SLAs, coach teams, and validate outcomes with clients.

Lead data engineering projects and teams to design, build, and maintain ETL/ELT pipelines and data architectures. Implement DataStage and cloud analytics solutions (AWS, Redshift, S3, Glue), manage DB2/SQL Server integrations and CDC/GoldenGate, deliver BI and reporting, mentor staff, manage client engagements, and ensure project success and technical standards.

The Data Engineer will design, develop, and deploy scalable AI-enabled data solutions, implement machine learning models, and conduct data analysis in the DoD's CDAO ADA IR program.

Senior Addepar data engineer/analyst responsible for supporting and scaling Addepar integrations, building and maintaining data pipelines (Addepar↔Snowflake/Canoe/other platforms), diagnosing and resolving data quality issues, developing SQL-based reconciliations and automation, partnering with business and technology stakeholders, and supporting production operations, governance, testing, and documentation for wealth and asset management data.

As a Palantir Data Engineer, you'll work on data exploration and analytics projects, apply innovative methodologies, and support military missions using AI solutions.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

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.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.