Databricks Tech lead

Role & responsibilities

• Data Pipeline Development: Designing and building scalable, efficient, and reliable data pipelines to extract, transform, and load (ETL) data from various sources into data storage systems, such as data lakes or data warehouses.
• Provide leadership on the migration from Hadoop to Azure and on how we leverage data in multiple portfolios
• Data Modeling: Developing and implementing data models that optimize data storage, retrieval, and analysis, considering factors such as data volume, variety, and velocity.
• Data Integration: Integrating data from different sources, including databases, APIs, streaming platforms, and third-party systems, to ensure data consistency and accuracy.
• Data Transformation and Cleansing: Applying data transformation techniques to clean, preprocess, and enrich data, ensuring its quality and readiness for analysis and reporting.
• Data Warehousing and Storage: Designing and maintaining data warehouses or data lakes to efficiently store and manage large volumes of structured and unstructured data.
• Data Governance and Security: Implementing data governance practices and security measures to protect sensitive data, ensure data privacy, and comply with regulations such as GDPR or CCPA.
• Performance Optimization: Monitoring and optimizing data processing and query performance, identifying and resolving bottlenecks to improve data retrieval and analysis efficiency.
• Collaboration with Data Analysts and Scientists: Collaborating with data analysts and data scientists to understand their requirements and provide them with reliable, accurate, and well-organized data sets for analysis and modeling.

Preferred candidate profile

• Strong Programming and Scripting Skills: Proficiency in programming languages such as Python, SQL, or Scala, as well as experience with scripting and automation tools, for data manipulation, ETL processes, and pipeline development.
• Experience with Azure cloud, Azure data bricks, SQL , Spark, Scala or Python
• Experience in Jira, Github, DevOps tools
• Data Integration and ETL Tools: Familiarity with tools and frameworks for data integration and ETL processes, such as Apache Kafka, Apache Spark, Apache Airflow, or Talend.
• Data Modeling and Database Design: Knowledge of data modeling concepts, dimensional modeling, and schema design to create efficient and scalable database structures.
• Big Data Technologies: Experience with big data platforms and technologies like Apache Hadoop, Apache Hive, Apache HBase, or Apache Cassandra for handling large volumes of data.
• Cloud Platforms: Familiarity with cloud platforms and related services for data storage, processing, and analytics.
• Data Governance and Security: Understanding of data governance principles, data security best practices, and compliance requirements to ensure data integrity, privacy, and regulatory compliance.