Data Engineer(AI/ML)

Overview of 66degrees

66degrees is a leading consulting and professional services company specializing in developing AI-focused, data-led solutions leveraging the latest advancements in cloud technology. With our unmatched engineering capabilities and vast industry experience, we help the world's leading brands transform their business challenges into opportunities and shape the future of work.

At 66degrees, we believe in embracing the challenge and winning together. These values not only guide us in achieving our goals as a company but also for our people. We are dedicated to creating a significant impact for our employees by fostering a culture that sparks innovation and supports professional and personal growth along the way.

Overview of Role

As a Data Engineer specializing in AI/ML, you'll be instrumental in designing, building, and maintaining the data infrastructure crucial for training, deploying, and serving our advanced AI and Machine Learning models. You'll work closely with Data Scientists, ML Engineers, and Cloud Architects to ensure data is accessible, reliable, and optimized for high-performance AI/ML workloads, primarily within the Google Cloud ecosystem.

Responsibilities

• Data Pipeline Development: Design, build, and maintain robust, scalable, and efficient ETL/ELT data pipelines to ingest, transform, and load data from various sources into data lakes and data warehouses, specifically optimized for AI/ML consumption.
• AI/ML Data Infrastructure: Architect and implement the underlying data infrastructure required for machine learning model training, serving, and monitoring within GCP environments.
• Google Cloud Ecosystem: Leverage a broad range of Google Cloud Platform (GCP) data services including, BigQuery, Dataflow, Dataproc, Cloud Storage, Pub/Sub, Vertex AI, Composer (Airflow), and Cloud SQL.
• Data Quality & Governance: Implement best practices for data quality, data governance, data lineage, and data security to ensure the reliability and integrity of AI/ML datasets.
• Performance Optimization: Optimize data pipelines and storage solutions for performance, cost-efficiency, and scalability, particularly for large-scale AI/ML data processing.
• Collaboration with AI/ML Teams: Work closely with Data Scientists and ML Engineers to understand their data needs, prepare datasets for model training, and assist in deploying models into production.
• Automation & MLOps Support: Contribute to the automation of data pipelines and support MLOps initiatives, ensuring seamless integration from data ingestion to model deployment and monitoring.
• Troubleshooting & Support: Troubleshoot and resolve data-related issues within the AI/ML ecosystem, ensuring data availability and pipeline health.
• Documentation: Create and maintain comprehensive documentation for data architectures, pipelines, and data models.
  
  

Qualifications

• 1-2+ years of experience in Data Engineering, with at least 2-3 years directly focused on building data pipelines for AI/ML workloads.
• Deep, hands-on experience with core GCP data services such as BigQuery, Dataflow, Dataproc, Cloud Storage, Pub/Sub, and Composer/Airflow.
• Strong proficiency in at least one relevant programming language for data engineering (Python is highly preferred).SQL skills for complex data manipulation, querying, and optimization.
• Solid understanding of data warehousing concepts, data modeling (dimensional, 3NF), and schema design for analytical and AI/ML purposes.
• Proven experience designing, building, and optimizing large-scale ETL/ELT processes.
• Familiarity with big data processing frameworks (e.g., Apache Spark, Hadoop) and concepts.
• Exceptional analytical and problem-solving skills, with the ability to design solutions for complex data challenges.
• Excellent verbal and written communication skills, capable of explaining complex technical concepts to both technical and non-technical stakeholders.
  
  

66degrees is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to actual or perceived race, color, religion, sex, gender, gender identity, national origin, age, weight, height, marital status, sexual orientation, veteran status, disability status or other legally protected class.