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Remote role, mid-level popular title, metro talent pool, broad AWS/data stack requirements.
Core data engineering skills and cloud tooling are highly transferable across industries.
Explicit 5+ years, mandatory data engineering domain experience and specific AWS/Spark/Airflow tech stack.
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Architect, design, and deliver scalable, reliable data engineering solutions including data lakes, warehouses, and ETL/ELT pipelines using AWS technologies.
Own end-to-end delivery of data engineering initiatives and optimize pipeline performance, cost, and scalability.
Mentor engineers and drive best practices in data quality, governance, automation, and enable self-service analytics for business stakeholders.
5+ years of professional experience in Data Engineering with ownership of complex projects.
Strong proficiency in Python and SQL; hands-on experience with AWS data stack (Glue, Redshift, S3, Athena, Lambda, Kinesis/Kafka) and orchestration tools (Apache Airflow, AWS Step Functions).
Experience designing and implementing data lakes, data warehouses, scalable pipelines, and data modeling.
Work Experience Required: 5+ years; Location: Remote (Anywhere in India); Employment Type: Full-time
Technical leader capable of architecting end-to-end data engineering solutions and managing delivery in SaaS or product environments.
Experienced in modern data architecture patterns including streaming/event-driven pipelines and cloud-native data platforms mainly AWS.
Skilled in mentoring engineers and collaborating with cross-functional teams (Product, Analytics, Business) to translate complex requirements into scalable data solutions.