Data Engineer

Purchasing Power-posted 3 months ago

Full-time • Mid Level

Hybrid • Atlanta, GA

251-500 employees

Personal and Laundry Services

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The Data Engineer will build, scale, and optimize real-time data systems and pipelines using Apache Kafka (Confluent), AWS, and a modern data stack. Work hands-on with streaming, ETL, distributed infrastructure, and PostgreSQL to fuel analytics and product innovation while deploying AI/ML frameworks, agentic automation, and MLOps tools to enable powerful analytics, advanced modeling, and a responsive data infrastructure.

Architect and build real-time streaming pipelines with Kafka, Confluent Schema Registry, and Zookeeper, ensuring scalable, event-driven data platforms
Leverage AWS services: build and manage ETL/ELT workflows on Glue and EMR, deploy scalable workloads using EC2, and orchestrate storage in S3
Optimize and maintain PostgreSQL and other databases: schema design, advanced SQL, and performance tuning
Integrate AI/ML tools and frameworks (TensorFlow, PyTorch, Hugging Face) into data workflows; design pipelines to prepare and serve data for training and inference
Automate data quality checks, feature extraction, and anomaly detection using AI-powered data validation and observability tools
Collaborate with ML engineers to deploy, monitor, and continuously improve machine learning models within production data pipelines (batch and real-time), leveraging MLOps platforms (e.g., MLflow, SageMaker, Airflow, Kubeflow)
Experiment with vector databases and retrieval-augmented generation (RAG) pipelines to support LLM and GenAI initiatives
Build and optimize event-driven and cloud-native architectures, supporting scalable, reliable AI data products

Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field
3+ years of Data Engineering experience with hands-on Kafka (Confluent/OSS) and AWS experience
Hands-on with automated data quality, monitoring, and observability tools for AI/data workflows
Advanced SQL and strong database fundamentals in PostgreSQL and other traditional and No-SQL databases
Proficiency in either Python, Scala, or Java for workflow development and AI integrations
Proficient with synthetic data generation, vector stores, or GenAI data products
Experience integrating ML models into data pipelines, using frameworks like PyTorch, TensorFlow, and MLOps platforms (Airflow, MLflow, SageMaker, Kubeflow)

Hybrid work model (Onsite/Offsite)
Comprehensive benefits: medical, dental, vision, company paid Basic Life/AD&D
401k Retirement Plan
Flexible PTO
Career Development
Employee Purchase Program

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