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Tier-1 brand, mid-level analytics role, metro location, and broad data skill requirements drive high competition.
Core data engineering skills are transferable across industries, though media-specific analytics knowledge is beneficial.
Explicit 5–8 years requirement plus mandatory data engineering, big-data, BI tooling and CI/CD skills increases strictness.
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Lead end-to-end data analytics and visualization projects including requirements gathering, planning, and implementing full-stack data solutions (pipelines, data models, dashboards).
Design semantic layers and data models to support self-service analytics and flexible querying in collaboration with cross-functional teams.
Drive automation to reduce manual data manipulation, build reusable frameworks to increase team productivity, and create solutions for data anomaly detection and root cause analysis.
Bachelor's degree or higher in quantitative field (Computer/Data Science, Engineering, Mathematics, Statistics).
5-8 years of relevant experience in business intelligence/data engineering.
Proficiency in SQL (clean, optimized coding) and data warehousing concepts (star schema, SCD, ELT/ETL, MPP databases).
Experience with big data technologies (Spark, Hadoop, Snowflake), BI tools (Looker, Tableau, PowerBI), analytics platforms (Athena, Redshift, BigQuery), and version control (Git) with CI/CD pipelines.
Experienced in managing complex large enterprise data use cases especially in Sales and Financial Analytics including forecasting.
Capable of independently solving ambiguous problems, managing multiple projects under tight deadlines in agile environments.
Skilled at collaborating across teams to translate business requirements into scalable, trustworthy analytics solutions that drive product and business strategies.