Design and implement scalable data pipelines and processing systems on the Azure platform to ingest, transform, and clean large volumes of data. Collaborate with cross-functional teams, including data scientists and business stakeholders, to provide optimized data solutions and technical guidance.
Tasks:
Building data pipelines to ingest data from various sources such as databases, APIs, or streaming platforms. Integrating and transforming data to ensure its compatibility with the target data model or format
Designing and optimizing data storage architectures, including data lakes, data warehouses, or distributed file systems. Implementing techniques like partitioning, compression, or indexing to optimize data storage and retrieval. Identifying and resolving bottlenecks, tuning queries, and implementing caching strategies to enhance data retrieval speed and overall system efficiency.
Designing and implementing data models that support efficient data storage, retrieval, and analysis. Collaborating with data scientists and analysts to understand their requirements and provide them with well-structured and optimized data for analysis and modeling purposes.
Collaborating with cross-functional teams including data scientists, analysts, and business stakeholders to understand their requirements and provide technical solutions. Communicating complex technical concepts to non-technical stakeholders in a clear and concise manner.
Independence and responsibility for delivering a solution
Ability to work under Agile and Scrum development methodologies
Train and mentor junior data engineers, providing guidance and knowledge transfer
Designing and implementing data processing systems on Azure platform. This involves writing efficient and scalable code to process, transform, and clean large volumes of structured and unstructured data.
Building data pipelines to ingest data from various sources such as databases, APIs, or streaming platforms.
Integrating and transforming data to ensure its compatibility with the target data model or format.
Identifying and resolving bottlenecks, tuning queries, and implementing caching strategies to enhance data retrieval speed and overall system efficiency.
Collaborating with cross-functional teams including data scientists, analysts, and business stakeholders to understand their requirements and provide technical solutions.
What We're Looking For:
Databricks 4/5.
Apache Spark 4/5.
Programming – Data 4/5.
Software Development for DE – 4/5.
Azure Services – 3/5.
Technical Leadership – 3/5.
Architecture – Data Processing – 3/5.
GenAI for Data Engineering – 3/5.
CI/CD Automation 3/5.
Other:
4-6 years of professional experience in a similar role.
Very good level of communication including ability to convey information clearly and specifically to co-workers and business stakeholders.
Working experience in the agile methodologies – supporting tools (JIRA, Azure DevOps).
Experience or familiarity with other cloud technologies, data warehouses, data governance, and business analysis is a plus.
Independence and responsibility in delivering solutions.
Ability to work under agile methodologies.
Hands-on experience in designing and optimizing of data storage (data lakes, data warehouses, distributed file systems).
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