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Tier-1 brand, metro location, general Data Engineer title, and broad cloud/PySpark skillset increase competition.
Low — cloud, Python, PySpark and SQL data engineering skills are highly transferable across industries.
High due to explicit 8–13 years requirement and mandatory cloud, Python, PySpark, and SQL skills.
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Design, develop, and implement scalable, cost-effective data pipelines and integration solutions using cloud platforms (AWS preferred).
Own data pipeline projects end-to-end including scope, timelines, risk management, and ensuring data quality and integrity.
Collaborate with data analysts, scientists, and business stakeholders to meet data requirements and resolve complex data challenges.
8 to 13 years of work experience in data engineering or related field.
Proficiency in Python, PySpark, and SQL; experience with big data ETL performance tuning.
Hands-on experience with cloud platforms (AWS, Azure, or GCP) and ability to architect scalable data solutions.
Bachelor’s degree in Computer Science, Engineering, or related field. Certifications such as AWS Certified Data Engineer or Databricks Certificate are preferred but not mandatory.
Experienced senior-level data engineer with deep expertise in cloud-based data architecture, especially AWS.
Capable of independently managing end-to-end data projects and mentoring junior engineers.
Strong analytical skills with a track record of solving complex data engineering problems and improving pipeline performance.