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Data Optimizer Non Technical Jobs (NOW HIRING)

Fortive

Non-Technical Customer Support

Everett, WA · On-site +1

Act as a resource for non-technical questions on products, warranties, and available services ... Our forward-looking companies lead the way in software-powered workflow solutions, data-driven ...

New

JF Retail Services

Data Scientist II

El Segundo, CA · On-site

... and non-technical audiences * Regularly validate and refine models to ensure accuracy; proactively identify opportunities for optimization and propose data-driven solutions to enhance performance

Fabletics

Data Scientist II

El Segundo, CA · On-site

... and non-technical audiences * Regularly validate and refine models to ensure accuracy; proactively identify opportunities for optimization and propose data-driven solutions to enhance performance

E Logic

Technical Analyst

Harlingen, TX · On-site

$65K/yr

This role focuses on connecting platforms, streamlining workflows, improving data quality, and ... Participate in workflow re-engineering and process optimization initiatives. * Document technical ...

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Data Optimizer Non Technical information

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How much do data optimizer non technical jobs pay per hour?

As of Jun 19, 2026, the average hourly pay for data optimizer non technical in the United States is $52.10, according to ZipRecruiter salary data. Most workers in this role earn between $45.91 and $58.89 per hour, depending on experience, location, and employer.

How does a Data Optimizer (Non-Technical) typically collaborate with other departments to ensure data quality and usability?

A Data Optimizer (Non-Technical) often acts as a liaison between data users and technical teams, working closely with departments like marketing, sales, and operations to understand their data needs and challenges. They facilitate communication, gather requirements, and help interpret data outputs to ensure information is accurate, accessible, and actionable for stakeholders. Collaboration may include organizing meetings, documenting processes, and providing feedback to data engineers or analysts to improve data systems. This cross-functional role is crucial for ensuring that data-driven decisions are based on reliable and well-structured information.

What is a Data Optimizer (Non-Technical)?

A Data Optimizer (Non-Technical) is a professional who focuses on improving how data is organized, managed, and utilized within a company, without needing advanced technical skills like programming. Their role often involves evaluating data processes, ensuring data quality, and recommending improvements for better decision-making. They may also coordinate with technical teams, create reports, and help implement best practices for data handling. This position is ideal for individuals with strong analytical, organizational, and communication skills who want to work with data in a more strategic or administrative capacity.

What are the key skills and qualifications needed to thrive as a Data Optimizer (Non-Technical), and why are they important?

To thrive as a Data Optimizer (Non-Technical), strong analytical thinking, attention to detail, and a foundational understanding of data management principles are essential, typically supported by a bachelor’s degree in business, analytics, or a related field. Familiarity with data visualization tools, spreadsheet software like Excel, and basic reporting platforms is commonly required. Excellent communication, problem-solving ability, and collaboration skills help individuals effectively interpret data trends and work with diverse teams. These skills enable accurate data-driven decision-making and ensure data processes align with business goals.

What is the difference between Data Optimizer Non Technical vs Data Analyst?

AspectData Optimizer Non TechnicalData Analyst
Required CredentialsCertifications in data tools, basic analytics, or data managementDegree in statistics, mathematics, or related field; often certifications in data analysis tools
Work EnvironmentCollaborates with technical teams, focuses on data quality and process improvementAnalyzes data sets, creates reports, and provides insights to stakeholders
Employer & Industry UsageUsed across industries for data quality and process optimization rolesCommonly employed in finance, marketing, healthcare, and tech sectors for data-driven decision making

In summary, Data Optimizer Non Technical focuses on improving data quality and processes without deep technical analysis, while Data Analyst involves analyzing data sets to generate insights. Both roles require data-related certifications but differ in their core responsibilities and work environment.

Senior Payments Data Optimization Analyst

Senior Payments Data Optimization Analyst

Optimized Payments

Atlanta, GA • On-site, Remote

$82K - $104K/yr

Other

Posted 7 days ago

Job description

Who are you?

Are you a strategic mastermind and part organizational guru looking to join a fintech? Look no further! Optimized Payments is seeking a highly motivated and experienced Senior Payments Data Optimization Analyst to join our growing team.

OP is seeking an innovative and dedicated individual committed to both serving our customers and having fun! Driven, with an entrepreneurial spirit and a heart for fintech. We're looking for someone who wants to make a significant impact on the Company, Clients, and your Career. At Optimized Payments, our people embrace these qualities, so if this sounds like you then please read on!

The Role:

The Senior Payments Data Optimization Analyst serves as the technical business liaison between Optimized Payments' consulting team and internal Data Engineering and Data Science partners. The role focuses on transaction-level data, aggregated data, reporting accuracy, and performance optimization across our client base.

Approximately 75% of the role partners with technical teams on data quality, analytics, and optimization initiatives, with 25% supporting client-facing insights and consulting discussions across platforms such as Fiserv, Chase, Stripe, Adyen, Worldpay, etc.

While this is a hybrid position based in Atlanta, you will have the opportunity to work remote primarily.

Responsibilities:

Data Quality, Normalization & Governance

  • Identify, track, and escalate data discrepancies or integrity issues impacting analytics, KPIs, client reporting, and analysis.
  • Support data normalization efforts of authorization, clearing, settlement, and fee data across multiple processors, gateways, and client data sources.
  • Partner with data engineering teams to define data requirements, schemas, and transformation logic for payment analytics requirements and improve data pipelines.
  • Contribute to and maintain documentation of data definitions, KPI logic, and calculation methodologies.
  • Assist in maintaining consistent reporting standards and governance controls across clients.
  • Validate reporting outputs and ensure alignment between source transaction data and downstream dashboards.
  • Review source-to-target mappings, schemas, and transformations to ensure accuracy of downstream reporting.

Payments Performance & Optimization

  • Analyze transaction, authorization, decline, fee, and network data to identify optimization opportunities for existing clients.
  • Monitor and report on key payments KPIs (e.g., authorization rates, interchange impact, routing performance, fraud indicators).
  • Develop data-driven recommendations to improve payments performance and reduce cost for our clients.
  • Support testing and performance initiatives for clients (e.g., routing strategies, retry logic, tokenization adoption, configuration changes).
  • Quantify impact of optimization initiatives and track performance over time.

Client Support & Insights

  • Prepare client-facing reporting and performance summaries using our proprietary analytics platform.
  • Translate complex payment and operational data into clear, actionable recommendations.
  • Support consulting discussions with analytical context, benchmarking, and scenario modeling.

Cross-Functional Collaboration

  • Partner with Technology, Data Engineering, Product, and Client teams to implement and measure optimization initiatives.
  • Help define business requirements for reporting enhancements and data improvements.
  • Document findings, assumptions, and measurable outcomes of optimization efforts.

Skills and Qualifications:

  • Bachelor's degree in a related field such as Finance, Statistics, Economics, or Computer Science.
  • 3-5 years of experience in payments, analytics, finance, consulting, or related field.
  • Experience with card payments, interchange, authorization strategies, or network data is required.
  • Strong analytical skills and experience working with large datasets.
  • Proficiency in SQL and experience with BI tools (e.g., Tableau, Power BI, Looker).
  • Ability to communicate insights clearly to both technical and non-technical stakeholders.
  • Strong attention to detail and structured problem-solving approach.
  • Knowledge of credit card scheme regulations, disputes, and chargeback processes.
  • Familiarity with payment gateways, processors, or fraud tools, including cross-border and local payment methods.
  • Deep understanding of how these systems interconnect and the resulting impact on credit card payment success.
  • Understanding of A/B testing or performance experimentation frameworks.

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