Data Engineer with ML Engineering Experience - Highbrow LLC

Job Description

Education and Experience

· Bachelor's degree in Computer Science, Data Science, Engineering, or a related field. A Master's degree or relevant certifications (e.g., Google Professional Data Engineer) is a plus.

· 5+ years of experience in data engineering, with at least 2-3 years of experience in machine learning engineering or deploying ML models in production.

· Proven experience in building and maintaining scalable data pipelines, data warehouses, and infrastructure to support ML workflows.

Technical Skills:

· Proficiency in big data frameworks and tools such as Apache Spark, Hadoop, Kafka, and Airflow.

· Advanced skills in data modeling, ETL processes, and data pipeline automation, with a focus on performance and scalability.

· Experience with cloud platforms (AWS, GCP, Azure) and their data services, such as AWS Glue, Google BigQuery, or Azure Data Lake.

· Strong programming skills in Python, SQL, and experience with data query optimization.

· Familiarity with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn) and libraries for building and testing machine learning models.

· Knowledge of containerization and orchestration tools (Docker, Kubernetes) for deploying and managing ML models in production.

Machine Learning Engineering Skills

· Experience in feature engineering, data preprocessing, and building data pipelines to support ML training and inference.

· Knowledge of MLOps best practices for continuous integration, deployment, and monitoring of ML models in production.

· Familiarity with model lifecycle management tools such as MLflow, TFX, or Databricks to streamline ML workflows.

· Strong understanding of data versioning, reproducibility, and monitoring of ML models to ensure model integrity over time.

· Ability to work with structured and unstructured data, with hands-on experience in NLP, computer vision, or time-series data for machine learning applications.

Data Engineering Skills:

· Proficiency in data storage and warehousing solutions (e.g., Snowflake, Redshift, BigQuery) for scalable data architecture.

· Understanding of data governance, quality, and security best practices, including data lineage and compliance with regulations.

· Experience with data lake architecture and data partitioning strategies to support large-scale data analysis.

· Ability to optimize data infrastructure for low-latency access and high throughput, especially for real-time ML applications.

Communication and Collaboration Skills:

· Strong communication skills with the ability to work closely with data scientists, ML engineers, and product teams to align data infrastructure with business requirements.

· Collaborative mindset, with experience working in cross-functional teams to deliver end-to-end data and ML solutions.

· Ability to document data workflows, pipelines, and ML infrastructure, ensuring transparency and ease of knowledge sharing.

· Proven ability to understand and respond to the needs of diverse stakeholders, from technical teams to business leaders.

Additional Qualifications:

· Familiarity with A/B testing, experimentation frameworks, and data-driven evaluation of ML models.

· Knowledge of data privacy and security regulations (e.g., GDPR, CCPA) for responsible data management and ML practices.

· Experience in specific industries like Telcomunications is a plus.

· Passion for staying up-to-date on the latest in data engineering, ML tools, and techniques, with a proactive approach to continuous learning.