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Databricks Architect
2 months ago
In this position, the senior Databricks developer is in charge of resolving cutting-edge problems in the real world and leading a group of one or more junior developers toward the objective.
Responsibilities:
- Collaborate closely with the architect to find design solutions that satisfy both functional and non-functional needs.
- Engage in order to comprehend the artifacts of solution and architecture design.
- Promote reuse by putting shared assets into practice.
- Apply engineering standards, best practices, and procedures proactively.
- Give guidance and understanding of the roles and duties necessary for the functioning of the solution.
- Throughout the whole lifespan, identify, discuss, and take steps to mitigate risks, assumptions, issues, and decisions.
- takes into account the art of the feasible, weighs the impact and practicality of different solution possibilities, and makes recommendations for concrete plans.
- Exhibit a good ability to solve technical and analytical problems.
- the capacity to work and analyze at different abstraction levels.
- Outstanding technical abilities to facilitate the development of complicated, future-proof global solutions
- A bachelor's degree or its equivalent in a related field of work (such as engineering, CS, CE, CIS, IS, or MIS).
- keeps a careful eye on newly developed technology and how they could be used to improve services and goods.
- Collaborate with the architect and leads to find solutions that satisfy both functional and non-functional needs.
- demonstrated familiarity with pertinent industry standards and trends.
- Exhibit a good ability to solve technical and analytical problems.
- Excellent Python or Scala coding abilities are required, though Python is preferred.
- Experience in the field of data engineering is a must.
- Must have completed at least two Databricks projects from start to finish.
- Experience with Databricks, which are made up of the following components, is required.
- Delta Lake
- Knowledge of the Databricks Lakehouse idea and how it's applied in business settings is a must.
- Strong knowledge of data warehousing, as well as the many security and governance guidelines related to Databricks, is required.
- Must be familiar with cluster optimization and how different cloud services integrate with it.
- A solid understanding is required to build a complicated data pipeline.
- Work experience with both batch and streaming data pipelines is required.
- Must be well-versed in the Hive and Spark data processing frameworks.
- Must have experience with all major cloud services, including CosmosDB/DynamoDB, ASB/SQS, ADF/Lambda, Azure, AWS, and GCP, as well as cloud databases.
- Alright Have CI/CD expertise in order to construct the Databricks job pipeline.
- It's beneficial to know about DBT.
- It's beneficial to understand Docker and Kubernetes.
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