Back
Common data-engineer title, metro location, and broad non-niche skillset increase applicant competition.
Core data engineering skills (Python, SQL, Spark, cloud) are highly transferable across industries, so sensitivity is low.
Multiple mandatory cloud, big-data, and ETL skills required but no explicit years makes shortlisting moderately strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and maintain scalable data pipelines, ETL/ELT processes, data models, and data warehouse solutions ensuring data quality, integrity, and governance.
Develop and manage data solutions on cloud platforms like Azure, AWS, or GCP using big data technologies such as Spark, Databricks, Hadoop, and Kafka, optimizing for performance and cost.
Collaborate with Data Architects, Business Analysts, Data Scientists, and BI teams to translate business needs into technical solutions, support production deployments, and mentor junior members.
Strong proficiency in Python, SQL, and Spark.
Hands-on experience with Azure Data Factory, Azure Databricks, Synapse Analytics, or equivalent cloud technologies.
Experience with ETL/ELT frameworks, data warehousing concepts, and platforms like Snowflake, Azure Synapse, Redshift, or BigQuery.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in designing and optimizing enterprise-scale data pipelines and data warehouse architectures on cloud platforms.
Skilled in handling both structured and unstructured data processing with a focus on data quality, governance, and security.
Able to work cross-functionally with diverse stakeholders and provide technical mentorship within a data engineering environment.