The Data Operations Engineer is the guardian of the data lifecycle. They ensure that data flows from source to destination without interruption, maintaining the plumbing of the organizations data infrastructure
1. Orchestration & Workflow Engines
Apache Airflow: Deep understanding of Operators, Sensors, Hooks, and the Airflow UI to manage complex task dependencies.
Workflow Logic: Knowledge of how to handle retries, branching, and 'backfilling historical data.
2. ETL/ELT Methodologies
Data Ingestion: Familiarity with moving data from APIs, logs, and relational databases into data warehouses (like Snowflake, BigQuery, or Redshift).
Transformation: Understanding how data is cleaned and restructured during transit.
3. Scripting & Command Line
Python: Ability to read and perform 'hotfixes or minor adjustments to existing scripts.
SQL: Proficiency in writing queries to validate data quality and troubleshoot ingestion errors.
Linux/Shell: Comfort navigating servers and checking system logs.
4. System Monitoring & Reliability
SLA/SLO Management: Understanding the business impact of data delays.
Observability: Using tools (like Grafana, Datadog, or Airflow logs) to monitor system health and data throughput
