Data Architect

About the job

Job Description & Summary At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. In data engineering at PwC, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for developing and implementing data pipelines, data integration, and data transformation solutions. Responsibilities: About the Role: We are hiring sharp, hands-on Data Architect to lead the design and implementation of scalable, high-performance data solutions across both traditional and cloud-based data platforms. This role demands deep expertise in PySpark, SQL, Python and Data Modelling, along with a strong understanding of cloud platforms and modern data engineering practices. What you will do: · Design and implement scalable data pipelines for batch and near real-time use cases in cloud environments · Write optimized, complex SQL queries for data transformation and analysis in cloud data warehouses · Develop efficient Python and PySpark scripts for large-scale data processing and ETL workflows · Create and maintain data models in Databricks, ensuring data quality and consistency · Optimize queries and scripts over large-scale datasets (TBs) with a focus on performance and cost-efficiency · Implement data governance and security best practices in cloud environments · Collaborate across teams to translate business requirements into robust technical solutions Mandatory skill sets: ‘Must have’ knowledge, skills and experiences · 8+ years of hands-on experience in Data Engineering · Strong command over SQL, Python, and PySpark for data manipulation and analysis · Deep experience with data warehousing concepts and implementation in cloud environments (Azure/AWS) · Proficiency in data modeling techniques for cloud-based systems (Databricks, Snowflake) · Solid understanding of ETL/ELT processes and best practices in cloud architectures · Experience with dimensional modeling, star schemas, and data mart design · Strong analytical thinking and problem-solving skills Preferred skill sets: ‘Good to have’ knowledge, skills and experiences · Familiarity with data lake architectures and delta lake concepts · Familiarity with Snowflake/Databricks Data Warehouse · Knowledge of data warehouse migration strategies to cloud · Experience with real-time data streaming technologies (e.g., Apache Kafka, Azure Event Hubs) · Exposure to data quality and data governance tools and methodologies · Understanding of data virtualization and federated query engines · Understanding of performance optimization techniques for cloud-based data warehouses · Apache Airflow – Workflow orchestration and scheduling · Certifications in Azure or AWS or Databricks Years of experience required: 8-12 years Education qualification: o BE, B.Tech, ME, M,Tech, MBA, MCA (60% above)

Requirements

• Data Engineering
• SQL
• Python
• PySpark
• Data Modeling
• Cloud Environments

Qualifications

• Bachelor of Engineering
• MBA (Master of Business Administration)
• Bachelor of Technology

Preferred Technologies

• Data Engineering
• SQL
• Python
• PySpark
• Data Modeling
• Cloud Environments

About the company

At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life.