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Data Encoder Jobs in Dallas, TX (NOW HIRING)

Google Cloud AI Engineer

Dallas, TX · Remote

$57 - $76.25/hr

Data Proficiency: Advanced knowledge of SQL for BigQuery, Python for ML engineering, and data preprocessing techniques (scaling, encoding, imputation). Cloud Infrastructure: Hands-on experience with ...

Remote Certified Coder

Dallas, TX · Remote

$22.25 - $30.50/hr

... data validation requirements is preferred); Ability to code using an ICD-9-CM code book (without using an encoder); Strong clinical skills related to chronic illness diagnosis, treatment and ...

Senior Software Engineer

Dallas, TX

$121K - $159K/yr

Experience with data mining or machine learning techniques * Experience with text codec, encoding & web protocols * Bash, Scala, C, or Hive development experience * Experience with full stack ...

Senior Software Engineer

Dallas, TX · Remote

$125K - $165K/yr

Experience with data mining or machine learning techniques * Experience with text codec, encoding & web protocols * Bash, Scala, C, or Hive development experience * Experience with full stack ...

Remote Certified Coder

Dallas, TX · On-site +1

$22.25 - $30.50/hr

... data validation requirements is preferred); • Ability to code using an ICD-9-CM code book (without using an encoder); • Strong clinical skills related to chronic illness diagnosis, treatment and ...

Engineer

Plano, TX · On-site

$95K - $115K/yr

... data warehousing and SQL skills. • Hadoop, Kafka, Spark, Impala, Hive, HBase, Ozone etc. • ... encoding, & file formats. • Expert level knowledge of Cloudera Hadoop components such as HDFS ...

... encoders, Bayesian Regression, and Times Series Modeling. Experience using data with high-volume (1TB+) & high-dimensionality (500+ variables per schema), especially within a big data framework ...

Showing results 21-40

Data Encoder information

See Dallas, TX salary details

$9

$32

$72

How much do data encoder jobs pay per hour?

As of Sep 15, 2026, the average hourly pay for data encoder in Dallas, TX is $32.24, according to ZipRecruiter salary data. Most workers in this role earn between $12.10 and $60.73 per hour, depending on experience, location, and employer.

What is a data encoder?

A Data Encoder is responsible for inputting, updating, and maintaining accurate data in computer systems or databases. They ensure data integrity by verifying and correcting information as needed. The role often involves handling confidential records, organizing files, and generating reports. Strong attention to detail, typing skills, and familiarity with data management software are essential for this position.

What are the typical daily responsibilities of a data encoder?

A Data Encoder is primarily responsible for accurately inputting and updating information into digital databases or systems, often working with large volumes of data from paper or electronic sources. Typical daily tasks include reviewing documents for errors, verifying data for completeness and accuracy, and organizing files for easy retrieval. Data Encoders may also collaborate closely with other administrative staff or departments to ensure that records remain up-to-date and accessible. In some organizations, they also assist with basic data analysis or generate routine reports to support business operations.

What are the key skills and qualifications needed to thrive in the data encoder position, and why are they important?

To thrive as a Data Encoder, you need excellent attention to detail, fast and accurate typing skills, and a high school diploma or equivalent as a common minimum qualification. Familiarity with data entry software, spreadsheet applications like Microsoft Excel, and sometimes database management systems is typically required. Strong organization, time management, and the ability to work independently or as part of a team are valuable soft skills in this role. These skills are crucial for ensuring the accuracy, reliability, and efficiency of data processing tasks within various industries.

Can I become a data encoder with no experience?

Data encoder positions typically do not require prior experience, as training is often provided on the job. Basic skills in typing, attention to detail, and familiarity with computers or data entry software are helpful for starting in this role.

How much do data encoders typically make?

Data encoders typically earn an hourly wage ranging from $10 to $20, depending on experience, location, and the complexity of the data. Entry-level positions may pay closer to the lower end, while experienced encoders or those with specialized skills can earn higher wages. Many roles also offer part-time or flexible schedules.

Is a data encoder a good job?

A data encoder job involves inputting and updating information into computer systems, often requiring attention to detail and basic computer skills. It can offer steady work with flexible hours, but typically has low to moderate pay and limited advancement opportunities. The role is suitable for those seeking entry-level work in administrative or data management fields.

What are the most commonly searched types of Data Encoder jobs in Dallas, TX?

The most popular types of Data Encoder jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Data Encoder jobs?

Cities near Dallas, TX with the most Data Encoder job openings:

Infographic showing various Data Encoder job openings in Dallas, TX as of September 2026, with employment types broken down into 73% Full Time, 7% Part Time, 14% Temporary, and 6% Contract. Highlights an 52% In-person, and 48% Remote job distribution, with an average salary of $67,054 per year, or $32.2 per hour.

Google Cloud AI Engineer

Dallas, TX • Remote

$57 - $76.25/hr

Contractor

Posted 5 days ago


Job description

Google

Google Cloud AI Engineer

Remote

Google Cloud AI Engineer for Partner Flex

Role Overview

We are seeking a highly skilled Artificial Intelligence Engineer. This role is pivotal in establishing Googleʼs Data Cloud as the essential foundation for the "agentic era". You will be responsible for designing and deploying sophisticated AI agents and grounding them in unique business data to ensure trust and operational efficiency.

Core Responsibilities

Agentic Design & Implementation

Develop intelligent agents using Vertex AI Agent Builder to automate complex business workflows.

Leverage the Agent Developer Kit (ADK) to build and manage multi-agent systems that collaborate to solve end-to-end business challenges.

Implement tools like MCP (Model Context Protocol) Toolbox to securely connect agents to enterprise databases like BigQuery and Spanner.

AI on Data Strategy

Utilize Vertex AI for model training, tuning, and deployment, ensuring seamless integration with BigQuery for feature engineering.

Build and optimize streaming data pipelines (e.g., via Dataflow) to execute real-time inference using RunInference API or Vertex AI endpoints.

Ground AI models in live business context using vector engines within BigQuery or AlloyDB to eliminate "AI amnesia".

Operational Excellence (Soft Skills)

Active Participation: Show up promptly for all internal and client-facing meetings.

Transparent Communication: Provide regular, structured status updates to team members and stakeholders regarding project milestones and technical blockers.

Proactive Collaboration: Demonstrate the ability to ask for help when facing technical hurdles and contribute to a collaborative troubleshooting environment.

Consultative Approach: Navigate corporate environments to translate high-level business goals into robust technical architectures.

Technical Qualiffications

Vertex AI Mastery: Proven experience with Model Garden, Vertex AI Pipelines, and model evaluation.

Data Proficiency: Advanced knowledge of SQL for BigQuery, Python for ML engineering, and data preprocessing techniques (scaling, encoding, imputation).

Cloud Infrastructure: Hands-on experience with Google Cloud Storage and Vertex AI endpoints.

Emerging Tech: Familiarity with stateful real-time processing and the latest innovations in agentic architectures.

Preferred Experience

Background in financial services or retail to better understand industry-specific data logic (e.g., credit risk, royalty forecasting, or search relevance).

Knowledge of privacy and compliance standards for handling PII through masking and redaction.