Data Engineer

About the role

We're looking for a Data Engineer with 3–5 years of hands-on experience to take ownership of complex data infrastructure across client engagements. You'll go beyond building pipelines — you'll architect them, optimize them, and make key design decisions that shape how clients store, move, and use their data.

This role sits at the intersection of deep technical execution and client delivery. You'll be expected to work independently, mentor junior engineers, and lead the technical conversation with clients from requirements through to production.

What you'll do

• Design, build, and own end-to-end data pipelines across platforms including Databricks, Palantir Foundry, and cloud-native services
• Architect data models and schemas optimized for performance, scalability, and maintainability — including complex ERD design across multi-source environments
• Lead data integration efforts connecting disparate databases, APIs, and enterprise applications into unified data environments
• Implement and enforce data quality frameworks, validation rules, and pipeline monitoring to ensure reliability in production
• Develop and maintain orchestration workflows using tools such as Apache Airflow, dbt, or equivalent
• Build and deploy data-driven applications and dashboards on top of the infrastructure you design
• Lead technical discovery sessions with clients, translating complex business requirements into scalable data solutions
• Review and provide feedback on junior engineers' code, models, and pipeline designs
• Identify performance bottlenecks in existing pipelines and drive optimization efforts
• Maintain thorough documentation of architecture decisions, data lineage, and pipeline logic

Requirements

• 3–5 years of experience in data engineering or a closely related role
• Advanced proficiency in SQL, including query optimization, indexing strategies, and complex transformations
• Strong Python skills applied to data pipeline development, automation, and data processing
• Demonstrated experience with enterprise data platforms — Databricks, Palantir Foundry, Snowflake, or similar
• Deep understanding of data modeling methodologies including star/snowflake schemas, data vault, and dimensional modeling
• Hands-on experience building and maintaining production-grade ETL/ELT pipelines
• Familiarity with cloud infrastructure (AWS, Azure, or GCP) as it relates to data architecture and storage
• Experience with workflow orchestration tools (Airflow, dbt, Prefect, or similar)
• Ability to lead client-facing technical discussions and independently manage deliverables
• Strong written and verbal communication skills; comfortable translating technical concepts for non-technical audiences