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Tier-1 brand, mid-level data role in metro with broad skills raises applicant competition.
Core data engineering skills (SQL, Python, ETL, Spark) are highly transferable across industries.
Explicit 2+ years requirement plus mandatory SQL, ETL, and scripting makes screening strict.
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Own the architecture and design of enterprise data warehouse (DW) solutions across multiple platforms.
Design, implement, and maintain scalable ETL pipelines and data integration processes using SQL, Python, and Spark to support dynamic business needs.
Collaborate with business analysts and other teams to gather requirements and deliver high-quality reporting and analytics datasets influencing decision making.
Minimum 2 years of data engineering experience.
Proficient in SQL and at least one scripting language (e.g., Python, KornShell).
Experience with data modeling, data warehousing, and building ETL pipelines.
Work Experience Required: Minimum 2 years in data engineering.
Experience with big data tools and frameworks like Hadoop, Hive, Spark, and EMR is advantageous.
Familiarity with AWS services such as Redshift, S3, AWS Glue, Kinesis, FireHose, Lambda, and IAM management suggests a strong fit.
Comfort working on large-scale data platforms supporting reporting, analytics, and business intelligence in dynamic, high-demand environments.