Senior Data Ops Engineer

We are seeking an experienced Senior Data Ops Engineer with 5-7 years of hands-on expertise in managing and optimizing data infrastructure, processes, and tools. This role is critical for driving efficiency, scalability, and reliability across our data ecosystems. As a
seasoned professional, you will lead initiatives to enhance data pipelines, ensure data quality, and support advanced analytics and operational needs.
Key Responsibilities:
Lead Database Administration: Oversee and optimize relational databases,
including SQL Server (preferred), Oracle, and PostgreSQL, ensuring performance,
security, and scalability.
Cloud Infrastructure Management: Architect and manage multi-cloud environments, with a focus on AWS (preferred), Azure, and GCP, to meet business needs.
Orchestrate Data Workflows: Design and implement robust workflows using tools like Airflow, Prefect, or Dagster to handle complex data pipelines.
Data Ingestion and Transformation: Implement and manage tools like dbT and FiveTran to streamline data extraction, transformation, and loading processes.
Ensure Data Quality: Develop and enforce data quality standards using tools such as SODA, Great Expectations, or Atlan.
Cloud Data Warehousing Expertise: Build and maintain scalable, secure, and performant data warehouses, especially with Snowflake (preferred) and Databricks.
Infrastructure as Code (IaC): Implement and manage IaC using Terraform (preferred), CloudFormation, or Cloud Foundry for seamless infrastructure provisioning.
Enhance Observability: Leverage monitoring tools like Datadog, Prometheus, or Grafana for real-time data observability and infrastructure monitoring.
Support DevOps Operations: Utilize tools like Ansible, Chef, ServiceNow, and PagerDuty to enhance deployment, monitoring, and incident management processes.
Mentorship and Collaboration: Guide junior engineers and collaborate across teams to establish and maintain best practices in data operations.
Optimize Processes: Continuously evaluate and improve operational efficiency and pipeline performance using Python and SQL.