Back
Remote hire, popular data-engineer title, and mid-level seniority attract high candidate density.
Core data engineering skills are transferable across industries, though compliance knowledge increases sensitivity.
Multiple mandatory cloud/data technologies and governance requirements create strict technical filtering.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and implement scalable, secure data pipelines and data processing frameworks across multi-cloud environments (Azure, AWS, GCP).
Develop and maintain ETL/ELT workflows using tools like Apache Airflow, Spark, Apache Beam, DLT, DBT, and Snowflake.
Collaborate with data scientists and AI engineers to operationalize machine learning models and integrate AI services ensuring data quality and governance.
Work Experience Required: Not explicitly mentioned in the JD.
Must be based in India (location restriction).
Proficiency with cloud platforms (Databricks, Snowflake, AWS, Azure, or GCP) and data engineering tools such as Python, Apache Airflow, DBT.
Experience with AI/ML model deployment tools like MLflow, SageMaker, Vertex AI, or Azure ML and knowledge of data governance and compliance (GDPR, HIPAA, SOC2).
Experienced in building scalable, multi-cloud data engineering solutions focusing on automation, performance, and security.
Familiar with deploying and operationalizing AI/ML models in collaboration with data science teams.
Comfortable working in Agile, cross-functional teams with strong communication skills to bridge technical and business stakeholders.