Data Engineer
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Key skills for this role
About the Role
We are seeking an experienced Data Engineer to design, build, and operate scalable, secure, and reliable data pipelines and platforms that power analytics and AI solutions for c.
Key Skills for This Role
Responsibilities
- Design, build, and maintain scalable batch and streaming data pipelines (ETL/ELT)
- Build and optimize data models, databases, data warehouses, and lakehouse architectures
- Develop and tune distributed processing jobs using Spark/PySpark
- Ensure data quality and data governance across the full data lifecycle
- Productionize pipelines with CI/CD, orchestration, monitoring, and alerting
- Build the data foundation for analytics and AI use cases
- Translate client business requirements into data architecture decisions
- Maintain technical documentation, data lineage, and delivery artifacts
Requirements
- Design, build, and maintain scalable batch and streaming data pipelines (ETL/ELT)
- Build and optimize data models, databases, data warehouses, and lakehouse architectures
- Develop and tune distributed processing jobs using Spark/PySpark
- Ensure data quality and data governance across the full data lifecycle
- Productionize pipelines with CI/CD, orchestration, monitoring, and alerting
- Build the data foundation for analytics and AI use cases
- Translate client business requirements into data architecture decisions
- Maintain technical documentation, data lineage, and delivery artifacts
Full Job Posting
Job Overview
- We are seeking an experienced Data Engineer to design, build, and operate scalable, secure, and reliable data pipelines and platforms.
- The role is hands on and client facing, owning data solutions end to end from architecture to production.
Key Responsibilities
- Design, build, and maintain scalable batch and streaming data pipelines (ETL/ELT).
- Build and optimize data models, databases, data warehouses, and lakehouse architectures (e.g., Databricks, Microsoft SQL Server).
- Develop and tune distributed processing jobs using Spark/PySpark.
- Ensure data quality and data governance across the full data lifecycle.
- Productionize pipelines with CI/CD, orchestration (e.g., Airflow, Azure Data Factory), monitoring, and alerting.
- Build the data foundation for analytics and AI use cases, collaborating with Data Scientists and AI Engineers.
- Translate client business requirements into data architecture decisions.
- Maintain technical documentation, data lineage, and delivery artifacts.
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