Top Remote Data Engineer Jobs in Baltimore, MD

The Snowflake Data Engineer will work on data cleansing and harmonization, moving from legacy systems to SAP S4, focusing on ETL/ELT development and view creation.

The AWS Data Engineer will work on a data cleansing and harmonization project, focusing on AWS ETL design and development using various AWS services.

This role involves designing, building, and maintaining data pipelines on Snowflake. Responsibilities include ensuring data quality and collaborating with stakeholders to deliver solutions.

The Snowflake Data Engineer will design, build, and maintain data pipelines on Snowflake, optimizing performance and ensuring data quality while collaborating with stakeholders.

The Snowflake Data Engineer and Product Owner will manage the Snowflake data analytics project, collaborating with stakeholders, leading Agile practices, and ensuring quality deliverables through effective team coordination and backlog management.

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 enterprise-scale data pipelines on Databricks and Azure. Implement batch, streaming, CDC, and incremental ETL/ELT solutions, Delta Lake optimizations, data quality, CI/CD, and secure, production-ready data products for analytics and AI/ML initiatives.

Maintain and monitor Snowflake data warehouse and ingestion pipelines. Troubleshoot file/load/data quality issues, operate SFTP/drop-zone/file automation, support scheduled loads, build dbt models and tables, document runbooks, and serve as backup for core warehouse operations while collaborating with engineering and IT.

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

Design, develop, and maintain enterprise data integration (ETL) systems and data platforms. Lead technical designs, optimize performance and scalability, support production issues and off-hours rotations, build reporting and BI solutions, document data processes, and collaborate with stakeholders and vendors to align business requirements.

Design, develop, and optimize ETL/data integration, data platforms, and BI solutions. Lead technical designs, troubleshoot production issues, participate in on-call support, and document processes to ensure scalable, secure, high-performance enterprise data solutions.

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.

Design, build, and maintain scalable ETL/ELT pipelines and Lakehouse solutions; ingest and transform diverse data formats; optimize Databricks and SQL Server environments; implement data quality, lineage, and governance; support batch and streaming ingestion; collaborate with analysts and stakeholders to enable analytics (fraud/anomaly detection, financial oversight); troubleshoot and improve pipelines. Candidates must be willing to undergo a U.S. government background investigation.

Senior Exasol data engineer to design, build, optimize, and validate SQL-based data pipelines for healthcare analytics. Responsibilities include complex query tuning, extracting/staging from SQL Server, chart validation, applying clinical terminologies, quality measure development, and maintaining disciplined version-controlled engineering practices.

Design, build, and maintain scalable ETL/ELT pipelines and data models using Databricks, Spark, dbt, SQL, and AWS services. Optimize ingestion, transformation, orchestration, and monitoring across structured and semi-structured data. Collaborate in Agile teams, support analytics and reporting for large-scale federal environments, and evaluate new data tools and platform improvements.

Design, build, and maintain scalable AWS-native data platforms and ETL/ELT pipelines using Databricks, Spark, Python, dbt, and SQL. Implement ingestion, transformation, orchestration, and analytics solutions (Iceberg, Parquet, Redshift, Athena), support FedRAMP/NIST controls, migrate legacy systems to cloud-native services, and mentor junior engineers while collaborating in Agile teams.

Responsible for developing data pipelines, ensuring data extraction performance, and collaborating in an Agile team to provide data-driven solutions.

Lead design and development of web UI and REST middleware within an Agile team. Define requirements and acceptance criteria, implement Java/Spring and Angular solutions, build CI/CD pipelines, work with Product Owner and testers, and produce/update design documentation.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Design, build, and maintain end-to-end batch and streaming data pipelines; develop ETL/ELT workflows and modular Python code; write and optimize SQL; implement analytics data models; monitor data quality, troubleshoot failures, and document pipelines; collaborate with stakeholders and mentor teammates. Remote role for candidates based in Latin America.

The Data Engineer will develop and maintain data pipelines, build data models, ensure data quality, and support cross-functional teams while improving internal data platform infrastructure and documentation.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.

The DMS Data Engineer is responsible for analyzing raw data, building data systems and pipelines, conducting complex data analyses, and collaborating on projects to enhance data quality and reliability.

Design, build, and maintain ETL pipelines, databases, and data models to process healthcare claims and related data. Ensure data quality, analyze claims data for trends, and produce reports and visualizations for nontechnical stakeholders. Collaborate with policy and analytics teams to translate requirements into data solutions and improve data infrastructure and analytics capabilities.

Design, develop, and support the Data Warehouse and reporting systems. Build and optimize SSRS reports, Power BI dashboards, and SQL ETL/queries. Ensure data accuracy, maintain documentation, and mentor team members while collaborating with business units to translate requirements into scalable data solutions.