CloudOps

Your key responsibilities

•   Lead the design, monitoring, and optimization of AWS-based data pipelines using services like AWS Glue, EMR, Lambda, and Amazon S3.
•   Oversee and enhance complex ETL workflows involving IICS (Informatica Intelligent Cloud Services), Databricks, and native AWS tools.
•   Collaborate with data engineering and analytics teams to streamline ingestion into Amazon Redshift and lead data validation strategies.
•   Manage job orchestration using Apache Airflow, AWS Data Pipeline, or equivalent tools, ensuring SLA adherence.
•   Guide SQL query optimization across Redshift and other AWS databases for analytics and operational use cases.
•   Perform root cause analysis of critical failures, mentor junior staff on best practices, and implement preventive measures.
•   Lead deployment activities through robust CI/CD pipelines, applying DevOps principles and automation.
•   Own the creation and governance of SOPs, runbooks, and technical documentation for data operations.
•   Partner with vendors, security, and infrastructure teams to ensure compliance, scalability, and cost-effective architecture.

Skills and attributes for success

•   Expertise in AWS data services and ability to lead architectural discussions.
•   Analytical thinker with the ability to design and optimize end-to-end data workflows.
•   Excellent debugging and incident resolution skills in large-scale data environments.
•   Strong leadership and mentoring capabilities, with clear communication across business and technical teams.
•   A growth mindset with a passion for building reliable, scalable data systems.
•   Proven ability to manage priorities and navigate ambiguity in a fast-paced environment.

To qualify for the role, you must have

•   5–8 years of experience in DataOps, Data Engineering, or related roles.
•   Strong hands-on expertise in Databricks.
•   Deep understanding of ETL pipelines and modern data integration patterns.
•   Proven experience with Amazon S3, EMR, Glue, Lambda, and Amazon Redshift in production environments.
•   Experience in Airflow or AWS Data Pipeline for orchestration and scheduling.
•   Advanced knowledge of IICS or similar ETL tools for data transformation and automation.
  •   SQL skills with emphasis on performance tuning, complex joins, and window functions.