Data Ops Engineer

We are seeking a highly skilled and motivated Data Ops Engineer with 2-5 years of experience to join our dynamic team. In this role, you will play a critical part in designing, implementing, and optimizing data infrastructure and workflows, ensuring efficient and reliable data operations across multiple environments. This is an excellent opportunity for a passionate professional to work on cutting-edge technologies, collaborate across teams, and contribute to innovative solutions in data engineering and operations.

KEY RESPONSIBILITIES
Administer and manage relational databases (e.g., SQL Server, Oracle, Postgres), ensuring optimal performance and availability.
Develop and maintain cloud infrastructure in AWS (preferred) or other cloud platforms like Azure and GCP, with an emphasis on multi-cloud environments.
Design, implement, and monitor data orchestration workflows using tools like Airflow, Prefect, or Dagster.
Facilitate data ingestion and transformation using tools such as dbT and FiveTran, ensuring data consistency and reliability.
Implement and maintain data quality and metadata solutions leveraging tools like SODA, Great Expectations, or Atlan.
Work with cloud data warehouses, particularly Snowflake (preferred) and Databricks, to support scalable analytics.
Leverage Infrastructure as Code (IaC) tools such as Terraform (preferred) or CloudFormation to manage cloud infrastructure.
Support DevOps workflows and automation using tools like Ansible, Chef, Service Now, and PagerDuty.
Enhance data observability using monitoring and visualization tools such as Datadog, Prometheus, or Grafana.
Collaborate across teams to ensure adherence to modern SDLC practices, including GitHub, Azure DevOps, JIRA, and Agile methodologies.
Write and optimize scripts and queries using Python and SQL.
Administer and manage relational databases (e.g., SQL Server, Oracle, Postgres), ensuring optimal performance and availability.
Develop and maintain cloud infrastructure in AWS (preferred) or other cloud platforms like Azure and GCP, with an emphasis on multi-cloud environments.
Design, implement, and monitor data orchestration workflows using tools like Airflow, Prefect, or Dagster.
Facilitate data ingestion and transformation using tools such as dbT and FiveTran, ensuring data consistency and reliability.
Implement and maintain data quality and metadata solutions leveraging tools like SODA, Great Expectations, or Atlan.
Work with cloud data warehouses, particularly Snowflake (preferred) and Databricks, to support scalable analytics.
Leverage Infrastructure as Code (IaC) tools such as Terraform (preferred) or CloudFormation to manage cloud infrastructure.
Support DevOps workflows and automation using tools like Ansible, Chef, ServiceNow, and PagerDuty.
Enhance data observability using monitoring and visualization tools such as Datadog, Prometheus, or Grafana.
Collaborate across teams to ensure adherence to modern SDLC practices, including GitHub, Azure DevOps, JIRA, and Agile methodologies.
Write and optimize scripts and queries using Python and SQL.