[India] - Cloud Data Engineer
At a glance
Mid-level Data Engineering role at ProcDNA. Gurugram · full-time.
Pay not stated Data Engineering salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and maintain ETL/ELT pipelines with Databricks and AWS or Azure cloud services
- Add transformation logic, validation rules and checks that keep datasets accurate and consistent
- Shape data models, schemas and warehouse structures for analytics and reporting use
- Track pipeline issues and tune performance, reliability, scalability and cloud costs
- Apply governance, security and compliance controls while working with cross-functional teams on data solutions
What you bring
- At least five years in data engineering, plus a B.Tech, BE or equivalent technical background
- Strong Python and SQL skills, with hands-on experience building cloud-based ETL/ELT pipelines
- Experience with Databricks or a comparable distributed processing framework, plus orchestration and Git
- Experience managing large datasets, data quality checks and ingestion, storage and consumption patterns
- Client and stakeholder management, as well as team management experience, is mandatory
Who this fits
This mid-level role suits someone who can build cloud data pipelines and manage data quality while working with cross-functional teams. Client, stakeholder and team management experience is required.
From the employer
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.
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