Explore 2 python data engineer jobs from 2 hiring companies in Arkansas City, KS, US. Filter by remote, salary, experience, and company, then open any listing to apply.

python-data-engineer

Python Data Engineer

Winfield

Ponca City

Wichita

Derby

Wellington

Blackwell

Haysville

Andover

Augusta

Mulvane

Newkirk

Clay Center

Rose Hill

Tonkawa

Pawhuska

Clearwater

Oxford

Belle Plaine

McCord

Python Data Engineer Jobs

INTRUST Bank

Knowmadics

Onsite

At INTRUST Bank, People Come First. Guided by strong character qualities—such as respect, compassion, humility, positivity, and initiative—we work to support one another, serve our customers, and strengthen our communities. Here, your career matters. You’ll find an environment where you’re encouraged to grow, empowered to lead at any level, and supported as you build a meaningful career with a trusted organization.
Job Summary: Responsible for leading and contributing to the design, implementation, and maintenance of data systems and integrations within an organization. Plays a key role in architecting and optimizing integration solutions to meet business needs. Promotes an environment of teamwork within the department and across the bank. Represents INTRUST Bank in all they do in the community and is focused on the development of new business relationships. Must maintain strict standards of confidentiality and perform duties in a manner supporting INTRUST’s Character Qualities.
Essential Functions: All expectations described in the job description’s essential functions are necessary to be successful in this role, performed with or without a reasonable accommodation. 
• Architects, develops, and maintains enterprise-grade data integration solutions and ETL/ELT processes with an emphasis on scalability, fault tolerance, long-term maintainability, and ongoing performance optimizations
• Leads the evaluation and adoption of database technologies and data storage strategies to provide recommendations on design and performance optimization at scale.
• Designs and governs API standards across the integration portfolio, encompassing RESTful, event-driven, and messaging-based patterns. Establishes versioning, security, and contract-testing practices that ensure reliable interoperability across systems and organizational boundaries.
• Owns the data modeling practice for integration and operational domains, producing designs that balance normalization, performance, and evolving business requirements. Provides guidance and mentorship to team members in the consistent application of data modeling principles.
• Drives cross-functional technical alignment by partnering with software development, data engineering, analytics, and business stakeholders to translate complex requirements into durable integration architectures.
• Establishes and maintains documentation standards for integration systems, APIs, and data models, ensuring documentation reflects current state and is sufficient to support onboarding, compliance requirements, and operational continuity.
• Leads architectural and code reviews across the team's integration portfolio, enforcing development standards, identifying systemic risk, and surfacing optimization opportunities. Owns and directs significant refactoring initiatives as warranted
• Defines, extends, and champions shared infrastructure and development patterns — including libraries, frameworks, templates, and tooling — that reduce duplication, accelerate delivery, and elevate baseline quality across the team.
• Evaluates emerging tools, technologies, and integration patterns through a structured assessment process, including prototyping and tradeoff analysis, and produces actionable recommendations that inform the team's technical roadmap and architectural direction.
• Defines and implements data quality rules (completeness, validity, timeliness, uniqueness) and automated checks within pipelines and integrations; establishes triage and remediation workflows to ensure data reaching downstream consumers meets defined standards. 
• Implements monitoring, alerting, logging, and tracing for integrations and pipelines; defines service objectives around availability, latency, and data freshness; and supports incident response and root-cause analysis to maintain operational reliability. 
• Designs and enforces security controls for data movement and APIs — including least privilege access, secrets management, encryption in transit and at rest, and token/key rotation — with consistent application of sensitive data handling and audit logging practices.
• Builds and maintains automated delivery practices for integration code, including testing gates, environment promotion, rollback strategy, and infrastructure/configuration as code where applicable. Plans and executes data and integration migrations — including platform transitions, refactors, and deprecations — with defined cutover strategy and validation criteria.
Education and Experience: Bachelor’s degree and/or equivalent work experience in computer science and/or 5+ years related job experience.
Required Skills and Knowledge: Substantial proficiency as a data engineer or data-focused software developer, with a proven track record of successfully leading and delivering complex data and integration projects. Expertise in working with SQL databases and data modeling concepts. Proficiency in programming languages for data manipulation and integration. Experience with cloud data warehousing platforms (Snowflake, Fabric, etc.) Strong knowledge of API design and development for building and consuming web services. Demonstrated leadership abilities, including mentoring, coaching, and leading technical teams. Excellent communication skills to convey technical concepts and collaborate with team members and stakeholders. Proven ability to analyze and solve complex data and integration challenges. Experience in project management, including planning, estimation, and execution. Ability to adapt to evolving data and integration requirements. Strong commitment to continuous learning and staying updated on emerging technologies and best practices in data engineering and integration. Strong penchant for critical thought and innovation. Values commitments from meeting times to meeting deadlines. Possesses a servant’s heart with humility towards building trusting relationships. Values people over technology and leverages technology to elevate the value of people for the company.
Required Licenses and/or Certifications: None.
Physical Demands: Extended period(s) of time may be spent viewing a personal computer, sitting, standing, and walking. Occasional lifting up to 25 lbs. The physical demands necessary for this role should be performed with or without a reasonable accommodation.
Working Conditions: Normal office conditions.
This job description summary is not intended to be an employment contract, nor is it intended to state or imply these are the only activities to be performed by the employee occupying this position.  Employees will be required to follow any other duties as assigned or requested by their supervisor.
INTRUST Financial Corporation and its subsidiaries, including INTRUST Bank, are Equal Opportunity Employers whose policy is not to discriminate unlawfully against any qualified employee or applicant for employment on the basis of protected military or veteran status, disability, race, color, religion, sex, age, national origin, pregnancy, genetic information, sexual orientation, gender identity, or any other classification protected by applicable local, state or federal law; except where an individual’s protected category constitutes a bona fide occupational qualification.

Senior Data & Integration Engineer

Data & Integration Engineer

Lead and contribute to design, implementation, and maintenance of data systems and integrations; architect and optimize integration solutions; mentor team; ensure data quality and security.

Job Purpose/Summary 
 
The MLOps Engineer designs, builds, and operates scalable machine learning systems that transform spatial-temporal and sensor-derived data into reliable ML workflows. This role spans the full ML lifecycle ingest, normalization, and feature engineering pipelines through distributed training and evaluation to low-latency inference and operational integration. 
  
Working across data infrastructure and deployment environments, the engineer operationalizes experimental models into reproducible, observable, and scalable systems. They ensure ML pipelines, containerized workloads, and CI/CD processes are robust, automated, and designed for real-world operational demands. 
  
In close collaboration with data scientists, geophysicists, and cross-functional engineering teams, this role translates research-grade algorithms into resilient services. As part of a fast-moving, government-funded technology business, the MLOps Engineer operates with high ownership in a low-ceremony, applied research environment, bringing structure, repeatability, and best practices to mission-driven sensor analytics systems. 
 
Duties and Responsibilities 
 
<ul>
<li>Design, build, and operate scalable ML and data pipelines for spatial-temporal and sensor-driven datasets. </li>
</ul>
<ul>
<li>Operationalize data science algorithms into reliable, distributed ML workflows covering feature extraction, training, evaluation, inference, and model lifecycle management. </li>
</ul>
<ul>
<li>Implement and maintain containerized ML workloads in cloud-native environments. </li>
</ul>
<ul>
<li>Integrate model outputs into downstream serving systems and analytical platforms to support web-based applications and operational decision-making. </li>
</ul>
<ul>
<li>Develop and maintain CI/CD pipelines for ML and data services. </li>
</ul>
<ul>
<li>Collaborate closely with data scientists to operationalize experimental models into reproducible, observable, and scalable production systems. </li>
</ul>
<ul>
<li>Take ownership of MLOps practices within an applied research team, bringing structure, repeatability, and best practices to evolving environments. </li>
</ul>
 
Qualifications 
 
Minimum: 
<ul>
<li>3+ years of experience in MLOps, ML Engineering, Data Engineering, or closely related roles building and running ML/data pipelines. </li>
</ul>
<ul>
<li>Strong Python data and ML stack experience, including tools such as Polars/Pandas, PyArrow, PySpark, NumPy/SciPy. </li>
</ul>
<ul>
<li>Experience integrating models built with frameworks such as PyTorch, TensorFlow, or Keras into scalable pipelines. </li>
</ul>
<ul>
<li>Demonstrated experience working with temporal data, ideally including sensor-derived signals. </li>
</ul>
<ul>
<li>Practical CI/CD experience for ML/data services using Git-based workflows. </li>
</ul>
<ul>
<li>Experience working in AWS or similar cloud environments.  </li>
</ul>
<ul>
<li>Experience running containerized ML or data workloads in Kubernetes. </li>
</ul>
<ul>
<li>Experience collaborating closely with data scientists to integrate algorithms. </li>
</ul>
<ul>
<li>Eligible to obtain a U.S. Security Clearance – U.S. Citizenship required. </li>
</ul>
Preferred: 
<ul>
<li>Direct hands-on experience with sensor datasets such as seismographic data, cellular sensor modalities, RF survey data, or GPS devices. </li>
</ul>
<ul>
<li>Experience deploying and scaling ML workloads in Kubernetes using KEDA or alternative event-driven autoscaling approaches. </li>
</ul>
<ul>
<li>Experience building event-driven or streaming pipelines e.g. Kafka, Spark, Flink, or Sedona feeding lakehouse-style open table formats e.g. Iceberg or Delta. </li>
</ul>
<ul>
<li>Experience with SQL query engines e.g. Trino, DuckDB, or Athena </li>
</ul>
<ul>
<li>Experience selecting and operating orchestration frameworks such as Airflow, Dask, Ray, or Spark for scalable ML workloads. </li>
</ul>
<ul>
<li>Strong PostgreSQL experience, ideally with TimescaleDB and/or PostGIS, integrating ML outputs into operational databases. </li>
</ul>
<ul>
<li>DevOps experience with Helm and GitOps tooling. </li>
</ul>
<ul>
<li>Background in defense, cybersecurity, space, or other mission-driven sensor analytics environments. </li>
</ul>
 
Working conditions 
 
<ul>
<li>Employees may be called upon to participate in in-person meetings, trainings, or company functions at Knowmadics offices or other designated locations. Travel in support of business operations may also be required, and employees are expected to comply with these obligations as part of their position. </li>
</ul>
 
Physical requirements 
 
May include sitting or standing for extended periods, working with computers and technical equipment, and occasionally lifting or moving materials or tools. 
 
Direct reports 
 
None

MLOps Engineer

3+ years in MLOps/ML/data engineering; Python stack; PyTorch/TensorFlow/Keras; AWS; Kubernetes; CI/CD; sensor data; eligible for U.S. security clearance.

Python Data Engineer Jobs in Arkansas City, KS, US | HiringCafe: every job on earth

Python Data Engineer Jobs in Arkansas City, KS, US

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