Staff Data Engineer
About The Position
As a Staff Data Engineer, you will lead the design and delivery of scalable, high-quality data pipelines that power analytics and reporting across the enterprise. This role combines deep hands-on engineering with technical leadership, driving best practices for data ingestion, transformation, and data modeling. You will play a critical role in building and optimizing data solutions using Databricks, Delta Lake, cloud-native technologies (AWS), ensuring reliable and efficient movement of data from source systems to curated data assets. This position focuses on delivering well-structured, trusted data through robust pipeline development and modern data engineering practices. You will also leverage AI-assisted development tools to improve coding efficiency, validation, and documentation.
Requirements
- 8+ years of experience in data engineering or data pipeline development
- Strong hands-on experience with Databricks, Apache Spark, and Delta Lake
- Advanced proficiency in SQL and Python for building and optimizing data pipelines
- Experience developing robust ETL/ELT pipelines and handling complex data transformations
- Hands-on experience with AWS cloud services (e.g., S3, EMR, Lambda, Glue, Redshift, Kinesis)
- Strong understanding of data modeling and data warehousing concepts
- Experience working with large-scale datasets (TB+ or greater) in distributed environments
- Knowledge of data quality frameworks, validation techniques, and monitoring practices
- Familiarity with CI/CD pipelines and modern development workflows
- Experience using AI-assisted development tools for code generation, validation, or documentation
- Strong problem-solving skills with the ability to debug complex data issues
- Acts as a technical leader and subject matter expert in data pipeline development
- Drives best practices for pipeline design, data transformation, and reliability
- Mentors engineers and elevates team capabilities through hands-on guidance and reviews
- Leads complex, cross-functional data initiatives with measurable business impact
- Balances hands-on execution with technical leadership
- Bachelor’s degree in Computer Science, Engineering, or related field
- 8+ years of relevant experience in data engineering
- Proven experience leading technical initiatives or large-scale data pipeline projects
Nice To Haves
- Master’s degree in a technical field
- Experience in large-scale, enterprise data environments
- Cloud certifications (AWS, Databricks)
Responsibilities
- Lead the design and development of scalable data pipelines and data products using Databricks, Spark, and Delta Lake
- Develop and optimize data transformations and ELT workflows using SQL and Python
- Design and implement data models and curated datasets to support analytics and reporting use cases
- Ensure data quality, consistency, and reliability through validation, monitoring, and testing practices
- Optimize pipeline performance, scalability, and cost efficiency within AWS environments
- Apply best practices for data partitioning, storage optimization, and query performance tuning
- Collaborate with product, analytics, and business teams to translate requirements into efficient data solutions
- Provide technical leadership and mentorship to engineers, including code reviews and design guidance
- Leverage AI tools for coding, validation, and documentation assistance to enhance productivity and code quality
- Troubleshoot and resolve data pipeline failures, latency issues, and data inconsistencies
- Continuously evaluate and improve data engineering workflows and tooling
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Number of Employees
251-500 employees
