Azure Data Engineering Lead

Capgemini•Atlanta, NY

11d•$99,712 - $168,716•Onsite

About The Position

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world. Job Location - Alpharetta GA / Berkeley Heights NJ (Day One Onsite - 5 days in a week) About The Role We are seeking a highly experienced and motivated Azure data engineering lead to join our growing data team. This includes tasks like building data pipelines, ensuring data quality, optimizing data models. The role requires expertise in Azure services like Azure Data Lake, SQL, Cosmos DB, Data Factory, Synapse Analytics, Databricks . You will be responsible for designing, developing, and leading data engineering initiatives with lakehouse architecture. You will work closely with cross-functional teams to understand business requirements and translate them into technical solutions. This role requires strong leadership and communication skills and mentoring data engineering teams.

Requirements

Extensive experience in data engineering, including data warehousing, data lakes, and lakehouses.
Lead the design and implementation of data pipelines for ingesting, transforming, and loading data into Azure data & analytics services for structured and semi structured data.
Experience in developing data services and pipelines with API integration for multitenant application.
Develop best practices including reusable code, libraries, patterns, and consumable frameworks for cloud-based data warehousing and ETL.
Design and implement efficient data models and database schemas for structured and unstructured data, optimizing storage and access for performance and scalability.
Strong analytical and problem-solving skills, with the ability to interpret complex data sets and derive meaningful insights.
Extensive experience with Azure data & analytics services, including ADLS, Azure SQL, Cosmos DB, Data Factory, Synapse Analytics, Azure Functions, Azure App Service, Apache Spark, Python, Databricks, and other related services.
Strong understanding and experience designing and development of data warehousing, data modeling, data integration, and ETL/ELT processes.
Proficiency in scripting languages like Python, SQL, and/or other languages relevant to data engineering tasks.
Monitor and Optimize data pipelines and data models for performance, scalability, and cost-effectiveness, identify bottlenecks, and implement optimization strategies.
Understanding of data governance principles, data security best practices, and compliance requirements.
Experience with data modeling and database design principles.
Familiarity with data governance and security best practices.
Provide advanced troubleshooting and resolution of data pipelines and operations issues.
Experience with relational databases and data warehouse technologies.
Proven leadership skills, with the ability to lead, mentor, and motivate a team.
Oversee a team of data engineers, providing technical guidance, mentorship, and support.
Provide guidance and support to team members, lead, mentor, and guide data engineering teams, fostering a collaborative and high-performing environment.
Develop and maintain documentation, including design documents, data pipelines with governance, Standard Operating Procedures (SOPs) and training materials for the team.
Excellent written and verbal communication skills, with the ability to effectively communicate technical information to both technical and non-technical audiences.
Experience with Agile Methodologies

Responsibilities

Design, configure, and optimize data architectures and pipelines to meet business needs.
Develop and embed data engineering pipelines as part of multi tenant B2B SaaS product.
Work closely with cross-functional teams, to understand business requirements and translate them into technical solutions.
Migrating existing data to new data model, including logging, reconciliation, data quality and error handling.

Benefits

Flexible work
Healthcare including dental, vision, mental health, and well-being programs
Financial well-being programs such as 401(k) and Employee Share Ownership Plan
Paid time off and paid holidays
Paid parental leave
Family building benefits like adoption assistance, surrogacy, and cryopreservation
Social well-being benefits like subsidized back-up child/elder care and tutoring
Mentoring, coaching and learning programs
Employee Resource Groups
Disaster Relief