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Senior Data Engineer
2 months ago
Embark on a Data Transformation Journey with Rullion
Rullion is seeking a talented Data Engineer to join our team in embarking on an ambitious data transformation journey using Databricks, guided by best practice data governance and architectural principles.
About the Role
This is a 3-month contract with potential to be extended. The role is Hybrid, with one day a week being based in our office, this is negotiable. It is a full-time role, 37 hours per week.
Key Responsibilities
- Develop and Maintain Scalable Data Pipelines: Design, develop, and maintain scalable, efficient data pipelines within Databricks, continuously evolving them as requirements and technologies change.
- Build and Manage Enterprise Data Model: Build and manage an enterprise data model within Databricks.
- Integrate New Data Sources: Integrate new data sources into the platform using batch and streaming processes, adhering to SLAs.
- Document Data Pipelines and Systems: Create and maintain documentation for data pipelines and associated systems, following security and monitoring protocols.
- Ensure Data Quality and Reliability: Ensure data quality and reliability processes are effective, maintaining trust in the data.
Requirements
- Technical Expertise: Extensive experience of Python preferred, including advanced concepts like decorators, protocols, functools, context managers, and comprehensions.
- Database Knowledge: Strong understanding of SQL, database design, and data architecture.
- Data Governance: Knowledgeable in data governance, data cataloging, data quality principles, and related tools.
- Data Extraction and Aggregation: Skilled in data extraction, joining, and aggregation tasks, especially with big data and real-time data using Spark.
- Data Cleansing: Capable of performing data cleansing operations to prepare data for analysis, including transforming data into useful formats.
- Data Storage Concepts: Understand data storage concepts and logical data structures, such as data warehousing.
- Code Quality: Able to write repeatable, production-quality code for data pipelines, utilizing templating and parameterization where needed.
- Data Pipeline Design: Can make data pipeline design recommendations based on business requirements.
- Data Migration: Experience with data migration is a plus.
