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Job description
About ProcDNA
ProcDNA is a global consulting firm. We fuse design thinking with cutting-edge technology to create game-changing Commercial Analytics and Technology solutions for our clients. We're a passionate team of 475+ across 9 offices, all growing and learning together since our launch during the pandemic. Here, you won't be stuck in a cubicle - you'll be out in the open water, shaping the future with brilliant minds. At ProcDNA, innovation isn't just encouraged; it's ingrained in our DNA. Ready to join our epic growth journey?
What we are looking for
Looking for a motivated Cloud Data Engineer with 5–7 years of experience to design, develop, and maintain scalable data pipelines and cloud-based platforms. Hands-on experience with AWS or Azure, along with strong programming skills in Python and SQL, and a solid understanding of ETL/ELT processes, data warehousing, and data modeling is essential. Experience with orchestration, exposure to batch or real-time processing, and familiarity with CI/CD, Git, and basic DevOps/DataOps practices is desirable. Strong communication skills, a collaborative mindset, and the ability to work effectively with cross-functional teams are key expectations.
What you'll do
Design, develop, and maintain scalable ETL/ELT pipelines using Databricks and cloud platforms (AWS/Azure).
Implement data transformation logic, validation rules, and data quality checks to ensure accuracy, consistency, and reliability of datasets.
Develop and manage data models, schemas, and data warehouse structures to enable seamless data consumption for analytics and reporting.
Monitor, troubleshoot, and continuously optimize data pipelines for performance, scalability, reliability, and cost efficiency in cloud environments.
Ensure adherence to data governance, security, and compliance standards across all data processing and storage layers.
Collaborate closely with cross-functional teams to deliver scalable, high-quality, and business-aligned data solutions.
Must Have
Minimum 5-7 years of experience in a Data Engineering role with a B.Tech/BE or equivalent technical background.
Strong programming skills in Python and SQL with experience in building and optimizing data pipelines.
Hands-on experience building ETL/ELT pipelines and cloud data services (Azure or AWS).
Experience working with Databricks or similar distributed data processing frameworks (e.g., AWS Glue).
Familiarity with workflow orchestration tools like Airflow and version control systems such as Git.
Experience handling large datasets, implementing data quality checks, and understanding data ingestion, storage, and consumption architectures.
Good understanding of data quality, monitoring, and basic DevOps/DataOps practices, along with strong problem-solving and communication skills.
Client and Stakeholder management along with Team Management experience is mandatory.
This page is for Indian job seekers preparing for cloud engineer, DevOps, backend and solutions architect roles that list AWS as a skill, whether you are a fresher with a first certification or an experienced engineer moving into a senior role. It covers 60 questions in three levels: core services and concepts, then design and configuration choices for one to three years of experience, and finally scenario, security, cost and troubleshooting questions asked in senior rounds. Read each answer, explain it aloud in your own words, and try the CLI commands in a practice account with a billing alert set.
Questions you may be asked
What is the difference between an AWS Region and an Availability Zone?
Explain the AWS shared responsibility model.
What is Amazon EC2, and how do you read an instance type name like m7g.large?
What is Amazon S3, and what are its main storage classes?
