AI / Data Engineer (Mid-Level)
New
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Intertwine AssociatesData engineering
Source API remote eligibility restrictions: United StatesFull-TimeMiddle
SalaryThe anticipated base salary range for this position is $95,000-$125,000 per year.
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Job Details
- Experience
- 3+ years of experience developing or supporting advanced statistical, machine learning, or data pipeline solutions.
- Required Skills
- PythonSQLETLNumpyPyTorchAzurePandasscikit-learn
Requirements
- Have 3+ years of experience developing or supporting advanced statistical, machine learning, or data pipeline solutions.
- Be proficient in Python, including Pandas.
- Have strong SQL skills and experience integrating relational database sources.
- Have hands-on experience with Azure cloud environments.
- Have experience with ETL development using Python and Microsoft technologies.
- Have experience with data validation, schema enforcement, and quality assurance.
- Be familiar with open-source data processing libraries such as NumPy, scikit-learn, PyTorch, or TensorFlow.
- Be able to obtain an agency Public Trust clearance.
- A bachelor's degree, Ph.D., or equivalent professional experience in Data Science, Computer Science, Statistics, Engineering, or a related field is listed as an additional qualification.
- Microsoft Data Engineer certification, such as DP-203 or DP-700, or a related Azure certification is an additional qualification.
- Knowledge of scalable, secure data engineering patterns in regulated environments is an additional qualification.
- Experience with Microsoft Fabric or Azure AI Foundry in production is an additional qualification.
Responsibilities
- Develop production-grade ETL workflows using Python and Microsoft-based frameworks.
- Ingest, transform, and validate structured and unstructured data.
- Implement schema enforcement, data validation, and quality checks.
- Support Azure Data Lake Storage, Azure SQL, and Azure-based data services.
- Design workflow orchestration using Azure Data Factory or Microsoft Fabric / Foundry.
- Build Python-based data services using Pandas, PyTorch, TensorFlow, and related libraries.
- Develop API endpoints and microservices to support analytics and ML platform interoperability.
- Implement logging, monitoring, performance tuning, and operational reliability.
- Collaborate with data scientists, analysts, architects, and governance teams.
- Apply data governance best practices for compliance, reproducibility, and auditability.
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