Back
Tier-1 brand, mid-level generalist data role, metro location, and common tooling increase competition.
Skills are transferable across industries but require specific data tooling and cloud experience.
Explicit 3+ years and mandatory Spark, SQL, Airflow, and cloud experience enforce strict screening.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop and maintain automated data pipelines for ingestion, transformation, and delivery of analytics datasets.
Collaborate with data analysts, scientists, and product teams to understand requirements and ensure data flow and quality across systems.
Contribute to data modeling, validation, monitoring, and adoption of best practices including CI/CD and AI-assisted tooling to enhance team productivity.
3+ years experience in building, implementing, and maintaining data warehousing and analytics solutions.
Proficiency in distributed data processing frameworks such as Spark, Hive, or Iceberg and SQL for complex analytical workloads.
Experience with data pipeline development using Spark and orchestration tools like Airflow, and exposure to cloud platforms preferably AWS.
Work Experience Required: 3+ years; Educational qualifications and notice period: Not explicitly mentioned in the JD.
Experienced in analytics-focused data engineering within large-scale, commercially impactful environments.
Familiar with advanced data processing, data modeling, and cloud-based data architectures, with practical experience implementing performance optimizations.
Capable of leveraging emerging AI/LLM tools and automation to improve data workflows and team efficiency.