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Remote Data Engineer Jobs in Frederick, MD (NOW HIRING)

Anika Systems

Data Engineer

Leesburg, VA · On-site +1

$115.80K - $139.10K/yr

Anika Systems is seeking a skilled Data Engineer to design, build, and optimize scalable data ... This opportunity is 100% remote. The ideal candidate has hands-on experience with ETL/ELT pipelines ...

Anika Systems

Data Engineer

Leesburg, VA · Remote

$117.20K - $140.70K/yr

Anika Systems is seeking a skilled Data Engineer to design, build, and optimize scalable data ... This opportunity is 100% remote. The ideal candidate has hands-on experience with ETL/ELT pipelines ...

COFENSE

Data Engineer (Remote, US)

Leesburg, VA · Remote

$115.80K - $139.10K/yr

Work with Architects & Cloud Systems Engineers in designing Data Platform and Architecture * Substantial experience with SQL and no-SQL OLTP databases and OLAP data warehousing technologies ...

Viasat, Inc.

Data Engineer

Germantown, MD · On-site +1

$174K - $261K/yr

Our mission is to enable our community of data scientists, analysts, and developers to derive faster insights and reduce TTM in order to fuel Viasat growth. The day-to-day In this Software Engineer ...

ViaSat

Data Engineer

Germantown, MD · On-site +1

$174K - $261K/yr

Our mission is to enable our community of data scientists, analysts, and developers to derive faster insights and reduce TTM in order to fuel Viasat growth. The day-to-day In this Software Engineer ...

Viasat

Data Engineer

Germantown, MD · On-site +1

$174K - $261K/yr

Our mission is to enable our community of data scientists, analysts, and developers to derive faster insights and reduce TTM in order to fuel Viasat growth. The day-to-day In this Software Engineer ...

Anika Systems

Data Architect

Leesburg, VA · Remote

$65.25 - $84/hr

This opportunity is 100% remote. Key Responsibilities Enterprise Data Architecture & Engineering * Design and implement scalable enterprise data architectures leveraging AWS and Apache ecosystem ...

Anika Systems

Data Architect

Leesburg, VA · On-site +1

$64.50 - $83/hr

This opportunity is 100% remote. Key Responsibilities Enterprise Data Architecture & Engineering * Design and implement scalable enterprise data architectures leveraging AWS and Apache ecosystem ...

Anika Systems

Quality Assurance Engineer

Leesburg, VA · On-site +1

This opportunity is 100% remote. Key Responsibilities Test Automation & QA Engineering * Design, develop, and maintain automated QA frameworks for data pipelines, APIs, and analytics platforms using ...

Anika Systems

Quality Assurance Engineer

Leesburg, VA · Remote

This opportunity is 100% remote. Key Responsibilities Test Automation & QA Engineering * Design, develop, and maintain automated QA frameworks for data pipelines, APIs, and analytics platforms using ...

VIS IT Solutions Inc

MongoDB Analyst

Dulles, VA · Remote

Remote We are seeking a skilled MongoDB Analyst to manage, analyze, and optimize large-scale NoSQL ... developers, data engineers, and business analysts to support application and reporting needs ...

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Remote Data Engineer information

See Frederick, MD salary details

$44.2K

$129K

$176.5K

How much do remote data engineer jobs pay per year?

As of May 28, 2026, the average yearly pay for remote data engineer in Frederick, MD is $128,973.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,800.00 and $136,700.00 per year, depending on experience, location, and employer.

What Does a Remote Data Engineer Do?

As a remote data engineer, you focus on collecting, storing, and organizing large amounts of information. You work from home to design, develop, and maintain systems for the mining, warehousing, and processing of data. A data engineer communicates with employers, clients, or other data professionals to assess the needs of the project and develop and implement solutions to meet those needs. Data engineers also take steps to manage current database architecture and make updates when needed. Remote engineers typically handle their responsibilities in a cloud-based environment using “big data” tools, such as Amazon Web Services (AWS) and SQL.

What are the key skills and qualifications needed to thrive as a Remote Data Engineer, and why are they important?

To thrive as a Remote Data Engineer, you need strong programming skills in languages like Python or Scala, expertise in SQL, data modeling, and a background in computer science or a related field. Familiarity with cloud platforms (such as AWS, Azure, or GCP), big data tools (like Hadoop and Spark), and certifications in cloud or data engineering are highly valued. Excellent problem-solving, communication, and self-management skills help remote data engineers collaborate effectively and stay productive in a distributed environment. These competencies ensure reliable data pipelines, scalable solutions, and seamless teamwork, which are critical for organizational success in data-driven projects.

How do remote Data Engineers typically collaborate with other team members across different time zones?

Remote Data Engineers often work with cross-functional teams, including data scientists, analysts, and software engineers, many of whom may be located in different parts of the world. Collaboration is usually facilitated through project management tools, version control platforms, and regular virtual meetings. It’s common to have a mix of synchronous check-ins and asynchronous communication, allowing for flexible scheduling and efficient handoffs. Strong written communication skills and proactive status updates are essential for staying aligned with team objectives and project deadlines.

What is a Remote Data Engineer?

A Remote Data Engineer is a professional who designs, builds, and maintains data pipelines, databases, and data processing systems while working from a location outside of a traditional office. They collaborate with data scientists, analysts, and other stakeholders to ensure data is collected, stored, and made accessible efficiently and securely. Remote Data Engineers use programming languages like Python or Scala, work with technologies such as SQL, Hadoop, or cloud platforms, and address challenges related to data quality and scalability. Their remote role allows them to work for companies regardless of geographic location, often relying on virtual collaboration tools to stay connected with their teams.

What is the difference between Remote Data Engineer vs Remote Data Analyst?

AspectRemote Data EngineerRemote Data Analyst
Required CredentialsBachelor's in CS, Data Science, or related; SQL, Python, cloud certificationsBachelor's in Statistics, Data Science, or related; SQL, Excel, visualization tools
Work EnvironmentCollaborates with data engineering teams, cloud platforms, big data toolsWorks with business teams, dashboards, reporting tools
Industry UsageTech, finance, healthcare, e-commerceMarketing, finance, retail, healthcare
Common Search IntentBuilding data pipelines, data infrastructureData reporting, insights, visualization

Remote Data Engineers focus on designing and maintaining data pipelines and infrastructure, often requiring programming and cloud skills. Remote Data Analysts interpret data, create reports, and provide insights using visualization tools. While both roles work with data, their responsibilities and skill sets differ, making each suited for different career paths within data teams.

More about Remote Data Engineer jobs
What are the most commonly searched types of Data Engineer jobs in Frederick, MD? The most popular types of Data Engineer jobs in Frederick, MD are:
What are popular job titles related to Remote Data Engineer jobs in Frederick, MD? For Remote Data Engineer jobs in Frederick, MD, the most frequently searched job titles are:
What job categories do people searching Remote Data Engineer jobs in Frederick, MD look for? The top searched job categories for Remote Data Engineer jobs in Frederick, MD are:
What cities near Frederick, MD are hiring for Remote Data Engineer jobs? Cities near Frederick, MD with the most Remote Data Engineer job openings:
Remote Data Engineer jobs near you
Data Engineer

Data Engineer

Anika Systems

Leesburg, VA • On-site, Remote

$115.80K - $139.10K/yr

Full-time

Posted 4 days ago

Job description

Anika Systems is seeking a skilled Data Engineer to design, build, and optimize scalable data pipelines and platforms supporting federal clients. This role will play a critical part in enabling enterprise data strategies, supporting Office of the Chief Data Officer (OCDO) initiatives, and delivering high-quality, trusted data for analytics, reporting, and mission operations.
This opportunity is 100% remote.
The ideal candidate has hands-on experience with ETL/ELT pipelines, XBRL data processing, Apache Iceberg-based architectures, and advanced data optimization techniques such as materialized views and context-aware data engineering. This role also requires proficiency in AI tools and AI-assisted development workflows, along with experience building and deploying CI/CD pipelines for data and analytics platforms.
Key Responsibilities
Data Pipeline Development & ETL/ELT
  • Design, develop, and maintain robust ETL/ELT pipelines to ingest, transform, and deliver data across enterprise platforms.
  • Build scalable data ingestion frameworks for structured and semi-structured data, including XBRL filings and financial datasets.
  • Implement data transformation logic to support analytics, reporting, and regulatory use cases.
  • Ensure data pipelines are reliable, performant, and scalable in cloud environments.
  • Leverage AI-assisted development tools to accelerate pipeline development, testing, and optimization.
Cloud Data Platforms & Iceberg Architecture
  • Develop and manage data solutions leveraging AWS services (e.g., S3, Airflow, DAGs, Glue, Lambda, Redshift).
  • Implement and optimize Apache Iceberg table formats for large-scale, ACID-compliant data lakes.
  • Support lakehouse architectures that unify data lakes and data warehouses.
  • Optimize data storage and retrieval strategies for performance and cost efficiency.
  • Enable data platforms that support AI/ML workloads and downstream generative AI use cases.
CI/CD & DataOps Engineering
  • Design and implement CI/CD pipelines for data pipelines, infrastructure, and analytics code using tools such as GitHub Actions, GitLab CI, Jenkins, or AWS-native services.
  • Automate build, test, and deployment processes for ETL pipelines and data platform components.
  • Implement DataOps best practices, including version control, automated testing, environment promotion, and rollback strategies.
  • Ensure reproducibility, reliability, and governance of data pipeline deployments across environments.
  • Integrate AI-driven testing and monitoring tools to improve pipeline quality and reduce operational risk.
Data Optimization & Performance Engineering
  • Design and implement materialized views and other performance optimization techniques to improve query efficiency.
  • Tune data pipelines and queries for performance, scalability, and cost.
  • Implement partitioning, indexing, and caching strategies aligned to workload patterns.
XBRL & Financial Data Processing
  • Develop pipelines to ingest, parse, and normalize XBRL (eXtensible Business Reporting Language) data.
  • Support regulatory and financial data use cases requiring high accuracy and traceability.
  • Ensure alignment with data standards and validation rules for financial reporting datasets.
Context Engineering & Data Modeling Support
  • Apply context engineering principles to ensure data is enriched with meaningful metadata, lineage, and business context.
  • Collaborate with Data Architects to support data modeling, schema design, and entity relationships.
  • Enable downstream analytics and AI use cases by structuring data for usability, discoverability, and governance.
Metadata, Data Catalog, and Governance Integration
  • Integrate pipelines with enterprise data catalogs and metadata management systems.
  • Support automated metadata capture, lineage tracking, and data quality monitoring.
  • Ensure alignment with data governance frameworks and standards established by OCDO organizations, including AI data readiness and traceability.
Stakeholder Collaboration & Agile Delivery
  • Collaborate with data architects, analysts, and business stakeholders to understand data needs and deliver solutions.
  • Participate in stakeholder listening campaigns, workshops, and data discovery efforts.
  • Work in Agile teams to iteratively deliver data capabilities and enhancements.
  • Contribute to identifying and implementing AI-driven efficiencies and automation opportunities across the data lifecycle.
Required Qualifications
  • Bachelor's degree in Computer Science, Engineering, Data Science, or related field.
  • 5+ years of experience in data engineering, ETL development, or data platform engineering.
  • Strong hands-on experience with:
    • ETL/ELT tools and frameworks
    • AWS data services (S3, Glue, Lambda, Redshift, etc.)
    • Apache Iceberg and modern data lake architectures
  • Experience designing and implementing CI/CD pipelines for data platforms and ETL workflows.
  • Demonstrated proficiency using AI tools and AI-assisted development workflows (e.g., LLM copilots, automated code generation, pipeline optimization tools).
  • Experience processing XBRL or complex financial/regulatory datasets.
  • Proficiency in SQL and Python.
  • Experience implementing materialized views and query optimization techniques.
  • Understanding of data modeling concepts and metadata management.
  • Familiarity with data governance, data quality practices, and data readiness for AI/ML use cases.
  • Ability to work in Agile, DevOps-oriented environments.
  • U.S. Citizenship required; ability to obtain and maintain a federal clearance.
Preferred Qualifications
  • Experience supporting federal agencies such as SEC, DHS, Treasury, or Federal Reserve System.
  • Familiarity with data catalog tools (e.g., Collibra, Alation, ServiceNow).
  • Experience with Apache Spark, Kafka, or other distributed data processing frameworks.
  • Experience enabling data pipelines for AI/ML or generative AI applications.
  • Knowledge of data maturity frameworks (e.g., EDM DCAM, TDWI).
  • Exposure to context engineering or semantic data layer design.
  • AWS or data engineering certifications.
  • Experience with infrastructure-as-code (IaC) tools (e.g., Terraform, CloudFormation) in support of CI/CD pipelines.

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