At a glance
Automatically prepared from the listing. Check the original description for the full requirements.
Jump to the original descriptionResponsibilities
Design, develop, and optimize scalable data pipelines using Databricks and AWS services. Lead data ingestion, transformation, and modeling initiatives while mentoring engineers and driving architecture standards.
Requirements
Requires 12+ years of experience in data engineering or architecture with expert knowledge of AWS and the Databricks/Spark ecosystem. Strong proficiency in data modeling, Python, SQL, and CI/CD practices is essential.
Working hours
40 hours per week
Skills
- AWS
- Databricks
- Python
- PySpark
- Delta Lake
- Unity Catalog
- Data Modeling
- ETL
- SQL
- Data Architecture
- CI/CD
- DevOps
- Data Pipelines
- Cloud Computing
- Distributed Systems
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Software
- Engineering
- Consulting
Keywords
- AWS
- Databricks
- Python
- ETL
- PySpark
- Delta Lake
- Unity Catalog
- Lakeflow
- S3
- Glue
- Lambda
- Step Functions
- Redshift
- Data Modeling
- Dimensional Modeling
- Data Vault
- Domain-Driven Design
- SQL
- CI/CD
- GitHub
- DevOps
- Data Engineering
- Data Architecture
- Distributed Data Systems
- Metadata Management
- Dbt
- Data Mesh
- Semantic Layers
- AI Readiness
- Enterprise Integration
Original job description
AWS Databricks Data Engineer – AWS / Python / ETL
Job Description
We are seeking a highly experienced Senior Data Engineer with strong expertise in Databricks, AWS, modern data architecture, and data modeling to design and build scalable enterprise data platforms. The role will lead the development of data pipelines, analytics assets, and data foundations supporting various enterprise data solutions. This is a senior-level, autonomous role requiring strong technical leadership, consultative problem-solving, and architecture expertise.
Key Responsibilities
• Design, develop, and optimize scalable data pipelines using Databricks (PySpark, Delta Lake, Unity Catalog, Lakeflow) and AWS (S3, Glue, Lambda, Step Functions, Redshift).
• Lead data ingestion, transformation, and modeling initiatives for enterprise data platforms.
• Define and implement robust data models supporting analytics, reporting, and AI/ML use cases.
• Gather and translate complex business requirements into scalable technical solutions.
• Establish data quality, monitoring, testing, and operational best practices across data platforms.
• Mentor engineers, drive architecture standards, and lead end-to-end solution delivery.
• Support strategic initiatives including AI readiness, data unification, metadata management, and enterprise integration programs.
Required Skills & Experience
• 12+ years of experience in Data Engineering, Data Architecture, or large-scale distributed data systems.
• Expert knowledge of AWS Data Services and Databricks/Spark ecosystem.
• Strong expertise in data modeling (Dimensional, Canonical, Data Vault, Domain-Driven).
• Advanced SQL and Python development skills with ETL/ELT experience.
• Experience with CI/CD, GitHub, DevOps practices, automated testing, and production deployments.
• Proven ability to work independently and lead solutions in ambiguous business environments.
Preferred Qualifications
• Experience in Asset Management, Wealth Management, or Financial Services.
• Knowledge of data quality frameworks, metadata management, dbt, semantic layers, or data mesh concepts.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field (or equivalent experience).
