Top Senior Data Engineer Jobs in Baltimore, MD

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Senior Data Engineer on PwC's Managed Data, Analytics & Insights team to design, build and manage advanced data ecosystems. Responsibilities include designing data solutions and scalable pipelines, solving complex problems, mentoring junior staff, maintaining high delivery standards, and building client relationships while aligning solutions to business context.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

As a Manager in Managed Services, you'll lead data ecosystems projects, mentor juniors, manage client accounts, and deliver data strategies to optimize operations and drive insights for clients.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

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.

Lead architecture and implementation of scalable batch and streaming ETL/ELT pipelines, data services, and graph-serving models for Observability & Insights. Own technical strategy, drive performance and cost improvements, enforce data governance and observability, lead cross-team initiatives, participate in on-call and incident follow-through, and partner with product and engineering to deliver low-latency graph exploration at extreme scale.

Design, build, operationalize, and maintain enterprise-scale data applications and scalable ETL/ELT pipelines. Develop and troubleshoot Palantir Foundry transformations, ontology objects, and logs. Collaborate with cross-functional teams to convert structured and unstructured data for analytics, monitoring, and platform operations.

Lead enterprise data platform strategy and architecture, define data standards and governance, drive Snowflake maturity, oversee legacy data migrations, design ingestion and DataOps patterns, and enable analytics, reporting, AI, and reusable data products across the organization.

Build and maintain scalable batch and streaming ETL/ELT pipelines, data models, and data warehouse architectures. Implement orchestration, quality, governance, and monitoring. Create self-service dashboards, enable ML/AI-ready datasets and feature pipelines, optimize performance, and collaborate with cross-functional teams to deliver analytics and AI-enabled solutions for customer experience and post-sales operations.

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 Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

The Manufacturing Analytics Engineer will manage the analytics pipeline, transforming operational data from OT systems into actionable insights for decision-making, while collaborating with Engineering, IT, and Operations.

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.

Lead design and implementation of a unified event-tracking data platform. Collaborate with Data, Product, and Engineering to build reusable event-publishing frameworks, monitoring/alerting, and deprecate legacy systems. Coach mid-level engineers, influence roadmap and processes, manage cloud/SaaS spend, create documentation, and participate in on-call rotations to ensure platform reliability and data trustworthiness.

Design and build end-to-end geospatial data systems: ingest and process satellite imagery into columnar/chunked stores, expose results via GraphQL/REST and FastAPI, build Next.js/React dashboards, automate with IaC and CI/CD, orchestrate pipelines with Prefect, optimize petabyte-scale I/O, and lead hiring, mentoring, and engineering best practices.

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.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Lead data engineering efforts within Technology Consulting: design data architecture and pipelines, implement AWS/Redshift and ETL solutions, support BI (QlikView/Oracle BI), coach teams, manage client relationships and SLAs, apply systems thinking to optimize outcomes and validate solutions with stakeholders.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

Design, build, and maintain scalable data pipelines and models; collaborate with analysts and data scientists to provision prepared datasets; implement data quality, governance and monitoring; recommend technologies; lead root-cause analysis, code reviews and testing; mentor teammates and support production data environments.

Lead data engineering engagements to design, build, and maintain ETL/ELT pipelines and cloud data architectures. Manage client accounts and mentor teams, leverage tools like DataStage, AWS/Redshift, DB2/SQL Server, GoldenGate, and BI/visualization platforms to deliver analytics, performance tuning, and scalable reporting solutions.

As a Data Engineer, you will develop solutions for customer-centric technology, ensuring platform integration and managing data architecture. Responsibilities include building data processes, maintaining data integrity, and collaborating cross-functionally to meet business challenges.

Design, build, and optimize scalable ETL/ELT data pipelines using Databricks and cloud platforms. Implement data quality checks, monitor and troubleshoot jobs, audit pipelines for compliance, and collaborate with analysts and business stakeholders to define data models and support reporting.