ZipRecruiter

Log In

2

Remote Data Optimization Jobs in Washington (NOW HIRING)

Anika Systems

Data Engineer

Leesburg, VA · Remote

$115K - $139K/yr

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 ...

TechFlow, Inc.

Senior Solutions Architect

Arlington, VA · On-site +1

$15K - $165K/yr

... remote data collection points. * Develop and document architecture strategies and technology ... optimization of technology solutions within the data center environment. * Conduct business and ...

Sparibis

Data Engineer

Washington, DC · On-site +1

$129K - $155K/yr

Location: 100% Remote Years' Experience: 5+ years Professional Experience Education: Bachelor ... Obtain data, formulate dataset processes, and store optimized data. * Identify problems and ...

TechFlow, Inc.

Senior Solutions Architect

Arlington, VA · On-site +1

$15K - $165K/yr

... remote data collection points. * Develop and document architecture strategies and technology ... optimization of technology solutions within the data center environment. * Conduct business and ...

Mondo

SQL Data Engineer

Centreville, VA · Remote

$125K - $140K/yr

Remote (EST/CST Preferred) Start Date Is: ASAP Duration: Permanent Compensation Range: $125,000 ... Advanced SQL skills including complex JOINs, CTEs, window functions, query optimization, and ...

next page

Showing results 1-20

All JobsData Optimization JobsRemote Data Optimization JobsRemote Data Optimization Jobs in Washington

Remote Data Optimization information

What is a Remote Data Optimization specialist?

A Remote Data Optimization specialist is a professional who works remotely to analyze, refine, and improve data systems and processes for organizations. Their main goal is to enhance the efficiency, accuracy, and usability of data, often by cleaning datasets, streamlining data flows, and implementing best practices for data management. They may use various tools and techniques to ensure data integrity and improve how data is stored, accessed, and utilized. These specialists often collaborate with data analysts, engineers, and business teams to support data-driven decision-making.

What is a data optimization job example?

A data optimization job involves analyzing and improving data quality, structure, and storage to enhance efficiency and accuracy. For example, optimizing database queries or cleaning large datasets using tools like SQL or Python helps organizations make better data-driven decisions.

What is the highest paying job in data?

In data-related fields, roles such as Chief Data Officer, Data Science Director, and Machine Learning Engineer tend to have the highest salaries, often exceeding six figures annually. These positions typically require advanced skills in data analysis, machine learning, and leadership, along with relevant certifications and experience.

What are some common challenges faced by professionals in remote data optimization roles, and how can they be addressed?

Remote data optimization professionals often encounter challenges such as coordinating with distributed teams, ensuring data accuracy across different systems, and managing time effectively without in-person supervision. To address these, it's important to establish clear communication channels, use collaborative tools for data sharing and project tracking, and set regular check-ins with team members. Additionally, staying updated on best practices and automation tools can help streamline workflows and enhance data quality, making remote work more efficient and productive.

Is it possible to get a remote job as a data analyst?

Yes, remote data analyst positions are widely available across various industries. These roles typically require skills in data analysis tools like Excel, SQL, or Python, and often involve working with cloud-based platforms or collaboration tools. Many companies offer remote work options for data analysts, especially with experience in data visualization and reporting.

Is 40 too late for data science?

Remote Data Optimization roles often value skills and experience over age, and many professionals transition into data science later in their careers. Learning relevant tools like Python, SQL, and machine learning can help, and continuous education or certifications can improve job prospects regardless of age.

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

To excel as a Remote Data Optimization Specialist, you need a solid background in data analysis, strong proficiency in statistics, and experience with optimization techniques, typically supported by a degree in data science, mathematics, or a related field. Familiarity with data visualization tools (like Tableau or Power BI), programming languages (such as Python or R), and database systems is commonly required. Strong problem-solving abilities, attention to detail, and effective communication skills set top performers apart in this role. These competencies are vital for translating complex data into actionable insights and driving efficiency improvements from a remote environment.

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

AspectRemote Data OptimizationRemote Data Analyst
Primary FocusImproving data storage, retrieval, and processing efficiencyAnalyzing data to identify trends and generate reports
Required SkillsData management, database tuning, scriptingData analysis, visualization, statistical skills
CertificationsDatabase certifications, data management credentialsData analysis certifications, SQL proficiency
Work EnvironmentTechnical teams, IT departments, data warehousesBusiness units, marketing, finance teams

Remote Data Optimization specialists focus on enhancing data systems' performance, while Remote Data Analysts interpret data to support decision-making. Both roles require strong technical skills, but their core responsibilities differ significantly, making them distinct career paths within data management and analysis.

What are the most commonly searched types of Data Optimization jobs in Washington? The most popular types of Data Optimization jobs in Washington are:
What are popular job titles related to Remote Data Optimization jobs in Washington? For Remote Data Optimization jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Remote Data Optimization jobs in Washington look for? The top searched job categories for Remote Data Optimization jobs in Washington are:
What cities in Washington are hiring for Remote Data Optimization jobs? Cities in Washington with the most Remote Data Optimization job openings:
Remote Data Optimization jobs near you
Infographic showing various Remote Data Optimization job openings in Washington as of July 2026, with employment types broken down into 71% Full Time, and 29% Contract. Highlights an 100% Remote job distribution.
Data Engineer

Data Engineer

Anika Systems

Leesburg, VA • Remote

$115K - $139K/yr

Full-time

Posted 8 hours 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.

Apply