Senior Python Developer / Data Engineer / ML Pipelines

EITAcies, Inc.
Austin, TX, United States
4 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Working hours
Regular working hours
Job source

Tech stack

Airflow Amazon Web Services Microsoft Azure Cloud Computing Program Optimization Data Cleansing Information Engineering Extract Transform Load (ETL) Data Warehousing Database Queries Python (Programming Language) Machine Learning
+12 more
Software Engineering Workflow Management Systems Data Processing Feature Engineering Large Language Models Apache Spark Build Management Pyspark Kubernetes Apache Kafka Machine Learning Operations Data Pipelines

Job description

Senior Python Developer / Data Engineer / ML Pipelines 100% Remote role Responsibilities Design and build large-scale data pipelines for ingestion, transformation and processing Work on ETL/ELT workflows handling different types of data Build and maintain end-to-end ML pipelines from data preparation to deployment and monitoring Collaborate with data scientists to productionize ML models Work on feature engineering, training pipelines and model serving Ensure data quality, monitoring and pipeline reliability Optimize systems for performance, scalability and cost Contribute to clean, maintainable, production-grade Python code

Requirements

8+ years of software engineering experience with Python as primary language Strong background in data engineering (ETL/ELT, pipelines, data processing) Hands-on experience building and maintaining ML pipelines in production environments Experience with PySpark / Apache Spark Experience with workflow orchestration tools like Airflow, Dagster, or Prefect Good understanding of streaming/data processing systems (Kafka, Kinesis, etc.) Experience working with cloud platforms (AWS / GCP / Azure) Strong SQL skills and experience with data warehouses Comfortable working in a distributed/remote engineering setup Plus Experience with NLP or LLM-based systems Familiarity with MLOps tools like MLflow, Kubeflow, or similar Experience with feature stores Exposure to data privacy, PII detection, or compliance-related systems

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