Lead Data Engineer - Databricks
Aventis Solutions
Charlotte, NC, United States
about 1 month ago
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Role details
Contract type
Temporary to permanent
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
3 years minimum
Working hours
Regular working hours
Job source
Tech stack
Application Programming Interfaces (APIs)
Amazon Web Services
Microsoft Azure
Cloud Computing
Cloud Database
Data Architecture
Information Engineering
Data Infrastructure
Extract Transform Load (ETL)
Data Systems
Data Warehousing
Distributed Computing Environment
+7 more
Python (Programming Language)
Operational Databases
Standard Sql
Event Driven Architecture
Data Lakes
Data Pipelines
Databricks
Requirements
- Databricks Expertise: Strong commercial experience with Databricks, including data engineering, Lakehouse architecture, Delta Lake and production data workloads.
- Cloud: Experience across Azure, AWS or GCP.
- Data Engineering: Strong understanding of ETL and ELT, data pipelines, data modelling, data warehousing, batch and streaming architectures, data quality and optimisation.
- Engineering: Strong SQL or Python, with experience building scalable and maintainable data solutions.
- Modern Data Platforms: Experience working with large enterprise data environments, distributed processing, orchestration, APIs and cloud-based data infrastructure.
- Leadership: Comfortable taking technical ownership, influencing architecture and working closely with data, technology and business stakeholders.
About the company
Aventis Solutions is supporting a leading financial services organisation as it continues to scale a major data transformation.
We’re looking for a Data Engineering Lead with 3+ years Databricks expertise to join a large scale data platform programme in Charlotte, North Carolina.
You’ll be joining a sophisticated financial services environment where data sits at the heart of underwriting, analytics, risk, operations and decision making. The focus is on building, improving and scaling modern data infrastructure across a substantial enterprise data estate.
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