Sobre este puesto de Senior Data Engineer - Databricks en Shyftlabs
Position Overview
We are looking for a Senior Data Engineer with strong hands-on experience in Databricks and a solid understanding of Data Governance concepts. The ideal candidate should have 7+ years of overall experience, including a minimum of 5 years of Databricks experience, covering data engineering and/or Databricks infrastructure, along with knowledge of data governance principles and practices.
Job Responsibilities:
Design, develop, and maintain scalable ETL/ELT pipelines using Databricks, PySpark, Python, and SQL.
Work on Databricks infrastructure, including workspace configuration, clusters, compute resources, access controls, and performance optimization.
Apply Data Governance principles, including data quality, data lineage, metadata management, data cataloging, and access controls.
Work with Unity Catalog to manage data permissions, governance policies, and data assets.
Develop and manage Delta Lake tables using Medallion Architecture (Bronze, Silver, and Gold).
Integrate data from multiple sources, including databases, Amazon S3, files, and REST APIs.
Create, schedule, monitor, and optimize Databricks Jobs and Workflows.
Implement data validation, error handling, logging, monitoring, and data quality checks.
Optimize Spark workloads, SQL queries, and data processing pipelines for performance and scalability.
Support data discovery, metadata documentation, and governance standards across data platforms.
Collaborate with cross-functional teams to ensure data accuracy, consistency, security, and compliance.
Troubleshoot data pipeline and infrastructure issues and recommend improvements to platform reliability.
Contribute to enterprise data management and governance best practices.
Basic Qualification:
7+ years of overall experience in Data Engineering or a related data technology role.
Minimum 5 years of hands-on Databricks experience, including data engineering and/or Databricks infrastructure.
Mandatory understanding of Data Governance concepts, including data quality, metadata management, data lineage, data cataloging, data ownership, and access controls.
Practical knowledge of data governance principles and their application in data platforms.
Understanding of data governance frameworks, policies, standards, and data management best practices.
Experience or knowledge of implementing data quality checks, data lineage tracking, metadata documentation, and governed data access.
Strong expertise in Python, PySpark, and Advanced SQL.
Hands-on experience with the Databricks Lakehouse Platform, Delta Lake, Unity Catalog, Workflows, Jobs, Clusters, and Notebooks.
Experience developing ETL/ELT pipelines, data transformations, batch processing, and incremental data loading.
Good understanding of data warehousing and data modeling concepts.
Knowledge of data security, access management, and governance practices within data platforms.
Experience with Spark performance tuning, partitioning, file optimization, and query optimization.
Familiarity with Git, CI/CD practices, and production deployment processes.
Strong analytical, problem-solving, and communication skills.
