Top Remote Data Engineer Jobs in Baltimore, MD

Build and maintain scalable data pipelines and integrations using Databricks and Azure. Transform and model large structured and unstructured datasets to support reporting, analytics, and operational needs. Implement data quality, monitoring, and governance; troubleshoot pipeline performance; collaborate with stakeholders and mentor junior engineers.

Design, build, and maintain scalable, secure data architectures, models, and ETL pipelines. Integrate diverse data sources, optimize performance and data quality, implement governance, monitor production SLAs, and collaborate with data scientists, analysts, and product teams. Provide technical leadership, documentation, and training to improve engineering practices across the organization.

Lead a team of 5-6 data engineers to design and implement scalable data pipelines (Airflow), manage Snowflake data architecture, build real-time streaming solutions with Kafka/Spark Streaming, develop and integrate APIs, enforce code quality, and drive technical decisions and best practices.

Design, build, and maintain scalable ETL/ELT pipelines and product-level data architectures. Improve pipeline performance and data freshness, lead code/design reviews, mentor engineers, and collaborate with Data Science, Product, and Engineering to make data reliable and accessible.

Design, develop, and support enterprise analytical and operational data solutions (ETL/ELT, data models, integrations, reports). Lead metric/technical design, manage medium-to-large projects, provide production support, documentation, training, and mentor junior team members. Maintain certifications and stay current with EHR/ERP, cloud, SQL, and BI technologies.

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

Design and implement scalable data pipelines and orchestration on GCP to support AI/ML-driven mail intelligence. Build stream and batch processing, ensure data governance and quality, optimize workflows, create visualizations, and collaborate with US cross-functional teams to align technical strategy and recover from failures.

Design, develop, maintain, and optimize ADABAS database structures and data solutions. Support mainframe data processing (Natural, COBOL, JCL), perform migrations to modern platforms, tune performance, ensure data integrity/security, troubleshoot issues, and collaborate with developers, architects, and analysts on modernization, API/cloud integration, and data warehousing initiatives.

Build, maintain, and optimize cloud data pipelines and dbt models; design Airflow DAGs; implement dimensional models and BI dashboards; integrate healthcare data with HIPAA-compliant practices; contribute to RAG pipelines and MLOps workflows; review contractor code, monitor data quality, and document designs following governance standards.

Remote entry-level Big Data Engineer role building data acquisition and munging pipelines. Requires strong Python3 (NumPy, Pandas), advanced SQL, scalable algorithm and data-structure implementation on distributed clusters, converting code to functional paradigms, and the ability to learn PySpark or Kafka quickly. Product-development, startup experience and real-world pipeline projects preferred.

Design, build, and deliver a global analytics platform: manage data collection and pipelines, develop visualizations and reports, create semantic models, tune performance, collaborate with IT and business stakeholders, and implement governance for analytics products.

The Senior Data Engineer will design and build data pipelines and infrastructure to support data science and business intelligence solutions, utilizing AWS services and streaming technologies.

Design, build, and maintain scalable backend systems and data pipelines to generate point-of-care insights; collaborate with product and engineering stakeholders; mentor junior engineers; ensure solutions are secure, performant, maintainable, and support analytics and unified metrics collection.

The Senior Data Engineer will design and implement lakehouse architectures, optimize ClickHouse deployments, develop ETL/ELT pipelines, and create data models to support analytical and operational functions.

Lead scaling of Arbital's AWS/Databricks lakehouse and Spark pipelines, implement actuarial healthcare calculations, own Airflow orchestration and CI/CD, ensure data quality and HIPAA-compliant PHI handling, partner with data scientists to deploy ML models, and set platform architecture and engineering standards.

Own and evolve end-to-end data pipeline architecture across ingestion, transformation, modeling, and serving. Lead platform improvements (cost, compute efficiency, access control), CI/CD, monitoring, and incident response. Drive cross-team technical initiatives, mentor engineers, and integrate AI tooling to improve workflows while remaining hands-on building and debugging production pipelines.

Design, build, and optimize ETL pipelines and data platform architecture using Airflow, BigQuery, Python, and Spark. Lead technical decisions, mentor engineers, collaborate with product and stakeholders, and improve data-quality and testing frameworks to support data-driven products.

Own end-to-end data pipelines (ingest, dbt transformations, BigQuery warehousing) and consolidate the analytics stack into a single source of truth. Build and maintain dbt models with tests and documentation, establish data quality and observability, own product analytics and instrumentation, partner with GTM and marketing on reporting, and prepare for high-volume/streaming ingestion.

Design, build, test, and maintain scalable cloud-based data platforms and ETL/ELT pipelines (batch and near-real-time). Implement data models, data marts, governance, metadata, CI/CD and IaC, optimize pipeline performance, support migrations/DR, produce documentation, and collaborate with architects, DBAs, and stakeholders to meet federal security and audit requirements.

Lead design, build, test, and maintain scalable cloud data platforms and pipelines (ETL/ELT, batch and streaming). Ensure data quality, governance, security, CI/CD deployment, performance tuning, migration/archival, and mentor data engineering teams to support analytics and AI/ML use cases for a federal modernization program.

Lead design and execution of data migration, archival, and disaster recovery for a cloud-based court case management modernization. Own migration strategy, data lifecycle and retention, DR/HA design and drills, tooling evaluation, documentation, and coordination with Data Engineering, Governance, Security, and stakeholders to ensure auditable, resilient, and compliant migrations.

Design, build, and maintain production-scale data systems and web-scale datasets (Spark/Flink) to support personalization ML. Drive dataset/schema design, collaborate with algorithm teams, and improve distributed big-data processing for member experience.

Designs, develops, and maintains cloud and on‑prem data solutions for provider master data, roster integrations, contract/network participation tracking, ETL/ELT pipelines, reporting datasets, and dashboards. Provides production support, documentation, data modeling, and stakeholder collaboration to deliver validated analytics and BI products for network management and provider operations.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Design, build, test, deploy, and support enterprise data solutions for financial reporting, reserving, and claims. Develop and maintain pipelines, transformations, data models, and controls using SQL, Python, PySpark, and Microsoft Fabric. Automate reconciliations and data quality checks, partner with business stakeholders, perform testing, review code, mentor peers, and resolve production issues to improve reporting accuracy and operational efficiency.