Cloud Data Engineer

ProcDNA; Gurugram District; On-site; INR 1,000,000 - 1,400,000; Full time

Job summary

A global consulting firm is seeking a motivated Cloud Data Engineer to design, develop, and maintain scalable data pipelines and cloud-based platforms. The ideal candidate should have 2-4 years of experience in data engineering, strong programming skills in Python and SQL, and hands-on experience with AWS or Azure. Responsibilities include developing ETL/ELT pipelines using Databricks, ensuring data quality, and collaborating with cross-functional teams. Join us to shape the future with cutting-edge technology.

Qualifications

• 2-4 years of experience in a Data Engineering role.
• Strong programming skills in Python and SQL.
• Hands-on experience with cloud data services (Azure or AWS).
• Experience with building ETL/ELT pipelines.

Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines.
• Implement data transformation logic and validation rules.
• Develop and manage data models and schemas.
• Monitor, troubleshoot, and optimize data pipelines.
• Ensure adherence to data governance and security standards.
• Collaborate with cross-functional teams to deliver data solutions.

Skills

Cloud

AWS

ETL

Azure

Data Engineering

Education

B.Tech/BE or equivalent technical background

Tools

Databricks

AWS Glue

Airflow

Git

Job description

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 400+ across 6 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 2–4 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 2‑4 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.

Skills: cloud, aws, etl, azure, data engineering