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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 employer, mid-level data role, metro location, and broad sought skills drive high competition.
Core data engineering skills transfer across industries, though financial/regulatory experience is preferred.
Explicit 5-8 years plus mandatory Snowflake, PySpark, AWS, and ETL experience makes filters stringent.
Job Description
Structured overview of role & requirementsAbout This Role
Design and develop scalable ETL/ELT pipelines and cloud-native data platforms using Snowflake, PySpark, Python, SQL, and AWS services.
Optimize data ingestion, transformation, quality, performance, and implement data marts, semantic layers, and enterprise reporting solutions on Snowflake.
Leverage AI technologies and DevOps automation (CI/CD, Airflow, Terraform) to enhance data discovery, metadata management, and improve data engineering workflows.
Minimum Requirements
5-8 years of experience in Data Engineering, Data Warehousing, and Cloud Data Platform development.
Strong hands-on skills with Snowflake, PySpark, Python, SQL, and AWS services such as S3, Glue, Lambda, EMR, Athena, Redshift, ECS/EKS, and IAM.
Experience with ETL/ELT pipeline development, data ingestion, transformation, and performance optimization.
Not explicitly mentioned in the JD: educational qualifications, notice period, or strict onsite/location requirements.
Ideal Candidate Profile
Proven ability to build scalable and optimized data solutions in cloud environments, especially with Snowflake and AWS ecosystem.
Experience working with advanced AI/ML data technologies, including Generative AI, LLMs, vector databases, and AI-assisted data workflows.
Background in financial services or regulated industries and familiarity with data governance, compliance, and modern data architecture patterns (Data Mesh, Data Fabric).