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Hourly Data Analytics Jobs in Washington (NOW HIRING)

SES

Data Analytics Engineer

Reston, VA · On-site

$119K - $143K/yr

Systems Engineering Services is seeking a Data Analytics Engineer based out of the Reston-DMV area. This position will require hybrid work in office in the Reston Town Center. Top 5 Technical Skills:

Ryde Technologies, LLC

Data Analytics Engineer

Arlington, VA · On-site

$130K - $157K/yr

Job Overview We are seeking a Data Analytics Engineer to support our Federal Government Customer with delivering secure, cloud-based mission systems. The Engineer will contribute to the design ...

New

Highspring

Senior Consultant - Data & Analytics

Mclean, VA

$86K - $109K/yr

The Team - Data & Analytics Our Data & Analytics practice is comprised of functional and technical experts across data strategy, data engineering, analytics, and transformation. We help clients ...

Gigatec Engineering

Data Analytics Engineer

Annapolis, MD · On-site

$113K - $136K/yr

... data moving cleanly, efficiently, and on schedule for analytics and operational needs. Required Skills * Experience using the Linux CLI and Linux tools * Experience developing Bash scripts to ...

Mitre

Software Developer, Data Analytics

Mclean, VA · On-site

$115K - $139K/yr

Join the MITRE Data Analytics team where you will provide software development, algorithm development, and data analytics (to include big data analytics, data mining, and data science) to enable data ...

Pantheon Data

Data Analyst

Washington, DC · On-site

$70K - $100K/yr

Perform descriptive and diagnostic analytics. * Conduct research and data analysis. * Support reporting, performance management, and business intelligence activities. * Translate data into actionable ...

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Hourly Data Analytics information

What is hourly data analytics?

Hourly data analytics refers to the process of collecting, analyzing, and interpreting data on an hourly basis to monitor trends, performance, and patterns within that specific timeframe. This approach is commonly used in industries such as retail, IT, and manufacturing to spot hourly fluctuations, optimize operations, and make timely decisions. Hourly data analytics can help organizations quickly identify issues or opportunities and respond proactively, improving overall efficiency and outcomes.

What are the key skills and qualifications needed to thrive as an hourly data analyst, and why are they important?

To thrive as an Hourly Data Analyst, you need strong analytical abilities, proficiency with data manipulation, and a foundational understanding of statistics, often supported by a degree in a quantitative field. Familiarity with tools such as Excel, SQL, and data visualization platforms like Tableau or Power BI is typically required. Attention to detail, problem-solving, and effective communication are crucial soft skills that set top performers apart. These skills and qualities are important to ensure accurate data insights, efficient task completion, and clear reporting to stakeholders.

What are some common challenges faced in an hourly data analytics role, and how can I prepare for them?

In an hourly data analytics role, a common challenge is managing multiple short-term projects with tight deadlines, as tasks often shift based on immediate business needs. You'll need to quickly analyze datasets, generate actionable insights, and communicate findings to different stakeholders, sometimes with limited context. To succeed, focus on honing your time management, adaptability, and clear communication skills. Being comfortable with learning new analytics tools on the fly and collaborating with cross-functional teams will also help you thrive in this dynamic environment.

What is the difference between Hourly Data Analytics vs Data Analyst?

AspectHourly Data AnalyticsData Analyst
Work HoursTypically hourly, flexible shiftsUsually full-time, standard hours
CertificationsRelevant certifications (e.g., Google Data Analytics)Same certifications often required
Work EnvironmentContract or freelance settings, remote or onsiteCorporate or organizational settings, office or remote
Job ScopeProject-based, task-specificBroader analysis responsibilities

Hourly Data Analytics and Data Analyst roles share similar skills and certifications but differ mainly in work hours and employment type. Hourly Data Analytics offers flexible, project-based work, while Data Analysts often work full-time in organizational settings. Both roles require comparable analytical skills and certifications, making them closely related in the data industry.

What are the most commonly searched types of Data Analytics jobs in Washington? The most popular types of Data Analytics jobs in Washington are:
What cities in Washington are hiring for Hourly Data Analytics jobs? Cities in Washington with the most Hourly Data Analytics job openings:
Hourly Data Analytics jobs near you

Data Analytics Engineer

SES

Reston, VA • On-site

$119K - $143K/yr

Other

Posted 6 days ago

Job description

Systems Engineering Services is seeking a Data Analytics Engineer based out of the Reston-DMV area. This position will require hybrid work in office in the Reston Town Center.

Top 5 Technical Skills:

  1. Python
  2. SQL
  3. API /. SDLC
  4. Data Insights and Visualization
  5. Financial Services Domain Knowledge

Job Description:

Seeking an experienced Data Analytics Engineer / Business UAT Tester with 7+ years of analytics, monitoring, visualization, production support, and developer collaboration experience, including 5+ years validating business requirements, data outputs, reports, APIs, and applications before production deployment. Hands-on with GenAI-assisted data extraction, prompt-guided validation, report-ready JSON generation, summarization, trend analysis, anomaly detection support, and validation of generated narratives, tables, and charts. Provides white glove user onboarding and embedded, forward-deployed style support to help developers, QA, analytics teams, and business users operationalize GenAI-enabled reporting solutions in regulated enterprise environments.

  • Business UAT & production readiness: Plan, execute, and document UAT test cases, expected results, evidence, defects, regression validation, acceptance criteria, requirements traceability, user sign-off, and release-readiness decisions.
  • GenAI-enabled ingestion and reporting: Support developers and business teams in designing, testing, and validating dynamic data ingestion and report generation workflows, including source-to-report reconciliation and business-ready outputs.
  • GenAI output validation: Validate extracted data, nested JSON payloads, generated summaries, trend insights, anomaly detection outputs, and narrative, table, and chart results using human-in-the-loop review and business-rule checks.
  • JSON/API and data quality validation: Validate REST API request/response payloads, nested JSON, schema alignment, metadata completeness, SQL reconciliation, source-to-output accuracy, and Jira-supported defect resolution using Postman and Swagger/OpenAPI.
  • Forward-deployed stakeholder support: Work closely with business users, product owners, developers, QA, model risk, validation, analytics, and technology teams to clarify requirements, resolve rollout issues, and close feedback loops during delivery.
  • White glove onboarding and adoption: Create onboarding guides, SOPs, user guides, training materials, UAT artifacts, knowledge-transfer content, and adoption playbooks; facilitate walkthroughs, answer user questions, capture feedback, and coordinate early-life support.

Technical Skills

GenAI Skills: Prompt-assisted extraction, field/entity mapping, GenAI output validation, report-ready JSON generation, summarization, trend analysis support, anomaly detection review, exception handling, human-in-the-loop quality checks, and validation of generated narratives, tables, and charts.

SQL / Databases: Strong SQL for complex analytical queries, source-to-target validation, reconciliation, semi-structured data analysis, data quality checks, production-readiness testing, relational database concepts, and use of SQL workbench/query tools for testing and validation.

Python: pandas, NumPy, JSON parsing/transformation, dynamic ingestion support, report generation workflows, analytics automation, and pipeline testing.

JSON / APIs: REST APIs, request/response payloads, nested JSON, Postman, Swagger/OpenAPI, schema checks, metadata validation, and API testing.

Tools / Methods: Jira, Agile/Scrum methodologies, AWS cloud platforms, dashboards, visualization, model monitoring, documentation tools, developer collaboration, white glove onboarding, and forward-deployed enablement.

Experience & Qualifications

Experience: 7+ years of software development, analytics, data engineering, monitoring, visualization, production support, dynamic ingestion support, report-generation testing, and business enablement experience.

Business UAT: 5+ years validating requirements, test cases, defects, fixes, regression outcomes, generated reports, data outputs, user adoption needs, and production readiness with stakeholders and developers.

Regulated delivery: Financial services or regulated enterprise experience, including documentation, validation, model risk, analytics, governance reporting, stakeholder engagement, and enterprise delivery standards.

Education

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Engineering or a related quantitative field.

Jake Lutman

Techncial Recruiter

Systems Engineering Services

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