Data Engineer (Data Lake & AI/ML Focus)

Uplevyl Inc.; Mission (KS); On-site; USD 120,000 - 160,000; Full time

Job summary

Uplevyl Inc. in Mission, Kansas is seeking a Data Engineer to design scalable data lake architectures to support analytics and AI/ML initiatives.

This role focuses on building data pipelines, optimizing storage, and enabling efficient machine learning workflows, with collaboration across data science teams and a strong emphasis on governance and security.

Qualifications

• 5-8 years of experience in data engineering, focusing on AI/ML data workflows.
• Proven track record building and maintaining data lakes and pipelines for AI/ML.

Responsibilities

• Build and maintain data lakes to support large-scale data ingestion and storage.
• Develop ETL/ELT pipelines for real-time and batch processing of structured and unstructured data.
• Collaborate with data scientists to provide clean, organized data for AI/ML model training.
• Optimize data pipelines for machine learning workflows and model deployment.
• Ensure data governance, security, and compliance for AI/ML operations.
• Implement and support MLOps practices for automating AI/ML workflows.

Skills

Data Lake Design

AI/ML Integration

Data Pipelines

Big Data Tools

AI/ML Tools

Programming

SQL

Cloud Platforms

Tools

Apache Spark

Airflow

Kafka

Hadoop

Hive

TensorFlow

PyTorch

SageMaker

AWS

Azure

Google Cloud

Python

SQL

Scala

Java

Job description

Today, we stand at the dawn of a new era: the AI era. AI is the fastest-growing sector in the history of mankind. However, today, the development and application of this new AI world is predominantly in the hands of men. And, historically when products and services were made by men, they were designed with men in mind. Our workplaces, our products, our services, our technology, our roles in society, and much more, have been predominantly male-oriented and naturally did not directly address or reflect the specific needs of women.

That reality led to creating several significant societal gaps. Given that women have not achieved parity across many dimensions over the past 2000 years of our history, why do we think we will be better off as we embark on this new digital AI age? We won’t unless we women take ownership of shaping it. Finally, the power and promise of AI technology can be applied toward accelerating women’s inclusion and progress towards balance.

Uplevyl has developed an AI technology platform that accelerates women’s potential in the workplace and beyond, through the creation and leveraging of the world’s largest, curated, women-specific data sets.

JOIN US

When you join Uplevyl, you will not be just another cog in some massive corporate machine. No matter what you do, you will be playing a critical role in changing the future for women. At Uplevyl, you will be seen, heard, and respected. You will be asked for your ideas; to get involved. We are an ambitious company of extraordinary colleagues doing great work. We have the audacity to dream big and change the world for half of its population.

Together, we work towards this huge aspiration, every single day. Come join us.

Position Overview:

The Data Engineer will design and manage scalable data lake architecture to support both analytics and AI/ML initiatives. This role focuses on building data pipelines, optimizing data storage, and enabling efficient machine learning workflows.

Key Responsibilities:

• Build and maintain data lakes to support large-scale data ingestion and storage.
• Develop ETL/ELT pipelines for real-time and batch processing of structured and unstructured data.
• Collaborate with data scientists to provide clean, organized data for AI/ML model training.
• Optimize data pipelines for machine learning workflows and model deployment.
• Ensure data governance, security, and compliance for AI/ML operations.
• Implement and support MLOps practices for automating AI/ML workflows.

Required Skills:

• Data Lake Design: Experience with cloud platforms like AWS S3, Azure Data Lake, or Google Cloud Storage.
• AI/ML Integration: Experience supporting AI/ML workflows and model deployment.
• Data Pipelines: Expertise in Apache Spark, Airflow, and Kafka for data processing.
• Big Data Tools: Proficiency in Hadoop, Spark, and Hive.
• AI/ML Tools: Familiarity with TensorFlow, PyTorch, or SageMaker.
• Programming: Strong skills in Python and SQL; knowledge of Scala or Java is a plus.
• Cloud Platforms: Proficient in AWS, Azure, or Google Cloud for AI/ML infrastructure management.

Qualifications:

5-8 years of experience in data engineering, with a focus on AI/ML data workflows. Proven experience in building and maintaining data lakes and pipelines for AI/ML.