Lead Analytics Engineer - Automation and GenAI

Lead Analytics Engineer - Automation and GenAI

Join a high-performing Analytics Engineering and Insights team as a Lead / Senior Analytics Engineer to take the lead on Automation and GenAI initiatives.

You will be responsible for identifying, developing, and embedding AI and automation methodologies. Drive their AI roadmap, build scalable tool stacks, and lead the end-to-end implementation of AI projects. A core focus is on translating business problems into structured AI solutions that reduce manual work, surface insights, accelerate revenue, and foster trust in our AI products among diverse stakeholders.

The Role

Champion opportunities to apply AI and automation to improve efficiency and effectiveness across diverse business areas. Translate complex or ambiguous business challenges into clear, actionable solutions using the right AI models, APIs, and automation frameworks. Work closely with legal, marketing, and compliance teams to build understanding and trust in AI solutions.

Develop scalable playbooks and best practices that allow AI and automation initiatives to expand efficiently and create lasting capabilities across the organization.

Required background

A strong data analytics engineering background is critical for success in this role. It ensures you can design and implement reliable data pipelines, integrate AI models seamlessly into business systems, and build the technical foundations required for automation at scale. This technical depth enables informed decision-making, faster experimentation, and the ability to translate data into tangible business outcomes.

Technical experience and background

• Comprehensive understanding of the AI landscape, including LLMs, agents, APIs, vector databases, automation tools, and no-code solutions.
• Hands-on experience building and deploying AI workflows or micro-products, whether in a startup or larger organizational setting.
• 4+ years’ experience in data engineering or analytics engineering roles.
• Hands-on experience with dbt Core, Airflow, and Snowflake.
• Strong SQL skills and understanding of data modeling (Dimensional / ELT).
• Practical hands-on experience with data science tools including Spark, Python, R, TensorFlow, PyTorch, and SQL.

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Engineering and Analyst

Industries

Retail and Airlines and Aviation

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