US Remote
Experience: 6–9 years
AI experience is a must (agentic AI, LangChain, RAG, chatbots, etc.) and should clearly demonstrate hands-on projects or at least 1–2 years of direct experience in the domain
Data engineering experience would be good, including orchestration, data modeling, and platforms such as Databricks, Snowflake, or GCP
We are looking for a Lead Engineer to build and maintain scalable data pipelines and data platforms that support analytics, business intelligence, reporting, and AI initiatives. In this role, you will work closely with data architects, analysts, and business stakeholders to develop reliable data solutions and ensure high-quality data is available across the organization.
This is a hands-on engineering role focused on designing efficient data pipelines, improving data infrastructure, and enabling teams to leverage data effectively.
Responsibilities
Design, build, and maintain scalable data pipelines and ETL/ELT workflows to ingest and transform data from multiple sources
Develop and optimize batch and near real-time data processing pipelines for analytics and reporting
Build and maintain data warehouse and data lake structures to support business intelligence and analytics use cases
Implement and maintain data models that support efficient querying and reporting
Improve performance and scalability of data systems through query optimization, indexing, and partitioning strategies
Implement data quality checks, monitoring, and logging to ensure reliability of data pipelines
Exposure to AI initiatives and experience building data pipelines supporting AI workflows
Work with data architects and engineering teams to implement scalable data platform designs
Collaborate with analysts, BI developers, and business stakeholders to deliver data solutions that support business needs
Maintain documentation for data pipelines, data models, and data workflows