Data Engineer

• Design, develop, and maintain data solutions for data generation, collection, and processing

• Be a crucial team member that assists in design and development of the data pipeline

• Build data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems

• Contribute to the design, development, and implementation of data pipelines, ETL/ELT processes, and data integration solutions

• Hands-on experience designing, developing, and implementing AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector search, tokenization, prompt engineering, and AI-assisted development tools such as OpenAI Codex, GitHub Copilot, or equivalent platform.

• Take ownership of data pipeline projects from inception to deployment, manage scope, timelines, and risks

• Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs

• Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency

• Implement data security and privacy measures to protect sensitive data

• Leverage cloud platforms (AWS preferred) to build scalable and efficient data solutions

• Collaborate and communicate effectively with product teams

• Collaborate with Data Architects, Business SMEs, and Data Scientists to design and develop end-to-end data pipelines to meet fast-paced business needs across geographic regions

• Identify and resolve complex data-related challenges

• Adhere to best practices for coding, testing, and designing reusable code/component

• Explore new tools and technologies that will help to improve ETL platform performance

• Hands-on experience with big data technologies and platforms, such as Databricks, Apache Spark (PySpark, SparkSQL), workflow orchestration, performance tuning on big data processing

• Proficiency in data analysis tools (eg. SQL) and experience with data visualization tools

• Excellent problem-solving skills and the ability to work with large, complex datasets

• Solid understanding of data governance frameworks, tools, and best practices.

• Knowledge of data protection regulations and compliance requirements

• Knowledge of data protection regulations and compliance requirements

• Experience with ETL tools such as Apache Spark, and various Python packages related to data processing, machine learning model development

• Good understanding of data modeling, data warehousing, and data integration concepts

• Knowledge of Python/R, Databricks, SageMaker, cloud data platforms

• Hands-on experience designing, developing, and implementing AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector search, tokenization, prompt engineering, and AI-assisted development tools such as OpenAI Codex, GitHub Copilot, or equivalent platform.

• Hands-on experience designing, developing, and implementing AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector search, tokenization, prompt engineering, and AI-assisted development tools such as OpenAI Codex, GitHub Copilot, or equivalent platform.

• Certified Data Engineer / Data Analyst (preferred on Databricks or cloud environments)

• Certified Data Engineer / Data Analyst (preferred on Databricks or cloud environments)

• Excellent critical-thinking and problem-solving skills

• Good communication and collaboration skills

• Demonstrated awareness of how to function in a team setting

• Demonstrated presentation skills