About Business Unit:
The Automotive Practice at Epsilon is a rapidly growing team, driving growth for major players in the automotive industry - from Original Equipment Manufacturers (OEMs) to dealerships across North America. Part of a 1,600-member multinational team, the practice provides the automotive world’s largest service reminder platform, alongside agency services and digital media solutions. A leader in the automotive space, the team supports over 50% of auto dealerships in North America and maintains relationships with over 280 million customers. Home to innovation and ground breaking technology, our Auto team leads the game in developing outstanding software and solutions for hyper-personalized digital marketing.
Click here to view how Epsilon transforms marketing with 1 View, 1 Vision and 1 Voice.
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Design, build, and maintain scalable data pipelines for batch and real-time data processing.
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Develop and optimize ETL/ELT workflows to ingest data from multiple sources such as APIs, databases, and third-party systems.
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Build robust data integration frameworks to consolidate structured and unstructured data.
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Ensure pipelines are fault-tolerant, scalable, and high-performing.
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Work with unified data intelligence platforms (Databricks) and cloud data platforms (AWS) to manage storage, computing, and orchestration services.
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Implement data modeling strategies (dimensional modeling, star/snowflake schema) to support AI modeling.
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Establish data quality frameworks including validation, monitoring, and anomaly detection.
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Implement data governance policies, metadata management, and data lineage tracking.
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Ensure compliance with data security, privacy, and regulatory requirements (e.g., GDPR, internal data policies).
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Collaborate with Data Science team and business stakeholders to understand data requirements.
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Provide curated datasets and data models to support advanced analytics and machine learning.
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Implement DataOps practices including CI/CD pipelines for data workflows.
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Automate deployment, testing, and monitoring of data pipelines.
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Maintain version control and documentation for all data engineering artifacts.
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Master’s or Bachelor’s Degree in Computer Science, Engineering, Data Science, or related field.
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4-6 years of experience as Data Engineer, or ML Engineer deploying hands-on projects.
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3+ years of experience in Python, PySpark, Databricks, Data Pipelines, ETL Processes, Data Modeling, Relational and Non-relational databases (NoSQL is a plus).
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Good working experience in Cloud Technologies (AWS or Azure)
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Familiar with Data Lake Architecture, Integration of Structured and Unstructured data, ML Flow tools, Automated Unit test frameworks,
- Understanding of version control (Git) and software engineering best practices
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Working knowledge of Machine learning algorithms (supervised/unsupervised, model evaluation).
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Exposure to MLOps tools (Docker, CI/CD, model deployment) is a plus
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Good written and spoken communication skills.
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Great team player and ability collaborate with larger team.
