Technical Architect @ Atos

Roles and Responsibility :

The Senior Tech Lead - Databricks leads the design, development, and implementation of advanced data solutions. Has To have extensive experience in Databricks, cloud platforms, and data engineering, with a proven ability to lead teams and deliver complex projects.

Responsibilities:

• Lead the design and implementation of Databricks-based data solutions.
• Architect and optimize data pipelines for batch and streaming data.
• Provide technical leadership and mentorship to a team of data engineers.
• Collaborate with stakeholders to define project requirements and deliverables.
• Ensure best practices in data security, governance, and compliance.
• Troubleshoot and resolve complex technical issues in Databricks environments.
• Stay updated on the latest Databricks features and industry trends.

Key Technical Skills & Responsibilities

• Experience in data engineering using Databricks or Apache Spark-based platforms.
• Proven track record of building and optimizing ETL/ELT pipelines for batch and streaming data ingestion.
• Hands-on experience with Azure services such as Azure Data Factory, Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, or Azure SQL Data Warehouse.
• Proficiency in programming languages such as Python, Scala, SQL for data processing and transformation.
• Expertise in Spark (PySpark, Spark SQL, or Scala) and Databricks notebooks for large-scale data processing.
• Familiarity with Delta Lake, Delta Live Tables, and medallion architecture for data lakehouse implementations.
• Experience with orchestration tools like Azure Data Factory or Databricks Jobs for scheduling and automation.
• Design and implement the Azure key vault and scoped credentials.
• Knowledge of Git for source control and CI/CD integration for Databricks workflows, cost optimization, performance tuning.
• Familiarity with Unity Catalog, RBAC, or enterprise-level Databricks setups.
• Ability to create reusable components, templates, and documentation to standardize data engineering workflows is a plus.
• Ability to define best practices, support multiple projects, and sometimes mentor junior engineers is a plus.
• Must have experience of working with streaming data sources and Kafka (preferred)

Eligibility Criteria:

• Bachelors degree in Computer Science, Data Engineering, or a related field
• Extensive experience with Databricks, Delta Lake, PySpark, and SQL
• Databricks certification (e.g., Certified Data Engineer Professional)
• Experience with machine learning and AI integration in Databricks
• Strong understanding of cloud platforms (AWS, Azure, or GCP)
• Proven leadership experience in managing technical teams
• Excellent problem-solving and communication skills