Back
Metro Bangalore and common Data Engineer title increase competition, tempered by seniority and Databricks specialization.
Core Databricks/AWS/Spark skills are broadly transferable across industries.
Multiple explicit years requirements and mandatory Databricks/AWS hands-on experience make shortlisting highly strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and implement enterprise-scale Data Lake and Lakehouse architectures leveraging AWS and cloud data warehouse technologies like Amazon Redshift and Snowflake.
Develop and optimize scalable ETL/ELT pipelines and distributed data processing solutions using Apache Spark (PySpark) to ensure reliability, scalability, and cost efficiency of cloud data platforms.
Lead technical and architectural decisions, coordinate across teams during the SDLC, and mentor junior engineers to enforce best practices.
8-10 years of overall experience in Data Engineering; 8+ years specifically building cloud-native data platforms on AWS.
5+ years of hands-on experience with Databricks and Delta Lake.
Bachelor's degree in Computer Science, IS, Data Engineering, Data Science, or related field.
Strong proficiency in SQL and Python; demonstrated experience optimizing complex queries and tuning Spark jobs.
Experienced in managing enterprise-scale data lake and lakehouse architectures within a hybrid work setting, indicating ability to thrive in complex, evolving environments.
Proven leadership skills in driving architectural decisions and mentoring within Agile/Scrum teams.
Certified or near-certified in Databricks and AWS technologies suggests advanced technical expertise and commitment to ongoing professional development.