• Design, build, and maintain reliable, high-quality data pipelines and ETL/ELT processes that enable analytics, reporting, and data-driven product features across multiple domains.

• Implement scalable data models, storage patterns, and APIs that support batch and streaming workloads while ensuring data quality, accuracy, and consistency.

• Collaborate with software engineers, data scientists, and product teams to understand data needs, translate requirements into technical solutions, and deliver well-documented, reusable datasets.

• Develop, optimize, and monitor data workflows and jobs for performance, cost efficiency, and operational robustness, including alerting, logging, and failure recovery.

• Apply and extend standard data engineering practices for security, privacy, governance, and compliance, including metadata management, lineage, and access control.

• Safely integrate and operate AI/MLenabled solutions that improve outcomes, including familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products.

Minimum Qualifications:

• Bachelor's degree in Computer Science or a related technical field; or Equivalent related professional experience.

• 2+ years of relevant professional experience, modernize data stacks including Scala/Java ( Must have ), Python, Spark, Airflow and AWS Data Stack.

• Professional experience as a data engineer or similar role building and operating production-grade data pipelines, data models, or data services.

• Hands-on experience with at least one programming language commonly used in data engineering and with SQL for querying, transforming, and validating data.

• Experience owning and operating data workflows or services, including monitoring, troubleshooting, and improving reliability and performance.

Preferred Qualifications:

• Experience designing and optimizing large-scale data pipelines, including data modelling, partitioning, and performance tuning for high-volume datasets.

• Background working with distributed data processing, storage, or streaming technologies to support real-time and batch data products in production.

• Demonstrated track record of improving data quality, observability, and governance through automation, standards, and tooling across services or domains.

• Familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products, including safely integrating data for AI/ML enabled solutions.