Data Scientist and Engineer Position

Senior Data Engineer and Scientist

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About the Role:

Job Overview:

We are seeking a highly skilled Senior Data Engineer and Scientist to join our team. This role involves designing and implementing scalable data architectures, developing and optimizing data pipelines, and collaborating with cross-functional teams to drive business outcomes.

The ideal candidate will have extensive experience in data engineering, including 5+ years working with Snowflake, strong skills in SQL and Python for data manipulation and engineering, and demonstrated experience building and optimizing data pipelines specifically for Machine Learning model training and deployment.

This role requires a strong understanding of cloud computing, distributed processing, and data security. The successful candidate will be able to design, build, and maintain robust ELT/ETL processes within Snowflake and AWS, integrate diverse, large-scale data sources, and ensure excellent data governance, quality, and performance for general business intelligence.

Responsibilities:

• Data Pipeline Development: Create and uphold scalable, reliable, and secure data infrastructure essential for Machine Learning model training, validation, and inference in production settings.
• Feature Store Collaboration: Partner with Data Scientists to design, build, and maintain a centralized Feature Store, ensuring consistency, reusability, and low-latency access to features for model training and deployment.
• GenAI Data Handling: Develop and execute data ingestion and processing workflows for text and unstructured data, including methods for vectorisation and populating vector databases to support Retrieval-Augmented Generation (RAG) architectures.
• Core Data Engineering: Build and maintain robust ELT/ETL processes within Snowflake and AWS to integrate diverse, large-scale data sources, ensuring excellent data governance, quality, and performance for general business intelligence.
• MLOps Partnership: Collaborate closely with ML Engineers to operationalise data flows to and from MLOps platforms (e.g., SageMaker, MLflow), focusing on automating pipelines and managing data versioning.
• Query and Compute Efficiency: Optimize data storage and computing resources across the platform to speed up data science experiments and model training workloads, while managing costs effectively.
• Data Security and Compliance: Implement advanced security measures to safeguard sensitive data used in AI datasets, maintaining data integrity and confidentiality in line with governance standards.

Required Skills and Experience:

Essential Skills:

• Data Engineering Expertise: Extensive experience in a core Data Engineering role, including 5+ years working with Snowflake and strong skills in SQL and Python for data manipulation and engineering.
• Data Science Enablement: Demonstrated experience building and optimizing data pipelines specifically for Machine Learning model training and deployment.
• Cloud Computing: Solid practical experience with AWS services (S3, Lambda, EC2, ECS) for creating reliable, scalable cloud data solutions.
• Distributed Processing: Familiarity with distributed computing frameworks such as PySpark or Dask for large-scale data transformation.
• Data Security: Strong knowledge of RBAC security models and secure data connection protocols.
• Communication: Excellent communication and teamwork skills, with a proven history of effective collaboration with Data Science and Business teams.

Preferred Qualifications:

Ai/ml Specialization:

• MLOps/MLOps Platforms: Hands-on experience integrating data systems with MLOps platforms (e.g., MLflow, Amazon SageMaker) for production model deployment.
• Generative Ai Data: Experience with vector databases (e.g., Pinecone, ChromaDB) and familiarity with data preparation for LLMs and RAG architectures.
• Feature Store Expertise: Practical or theoretical knowledge of Feature Stores (e.g., Feast, Tecton) and their role in the ML lifecycle.
• Advanced Data Orchestration: Experience with modern orchestrators like Apache Airflow, Prefect, or Dagster for managing complex, data-science-focused workflows.
• Experience with Cortex AI or comparable cloud AI tools.
• Certifications in Snowflake, AWS Data Analytics, or related areas.

Benefits:

We Take Care of You:

We provide comprehensive health care coverage designed for your mind and body. Our flexible downtime policy ensures you have ample time to recharge. We invest in your continuous learning, offering a wealth of resources to grow your career and learn valuable new skills.

Invest in Your Future:

We offer competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs. Our family-friendly perks include discounts, referral incentives, and more.

Equal Opportunity Employer:

We value diversity, equity, and inclusion. All qualified candidates will receive consideration for employment without regard to race/ethnicity, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, military veteran status, unemployment status, or any other protected characteristic.

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