Data Engineer / Senior Data Engineer
ArcadiaFull Time
Senior (5 to 8 years)
Candidates should have over 5 years of experience building production-grade data pipelines and backend services, with strong expertise in Python and SQL. Familiarity with AWS technologies and modern data warehouses such as Snowflake, BigQuery, or Redshift is required. Experience with distributed architectures, an ownership mindset, urgency, and customer focus are also essential. PySpark experience is a plus, as is experience with Spark, Airflow, and Infrastructure as Code (IaC) solutions on AWS, Databricks, and Snowflake.
The Data Engineer will be responsible for ingesting and transforming raw customer data into high-quality datasets. They will design, build, and maintain scalable data pipelines and orchestration workflows, and preprocess metrics and metadata for anomaly detection and lineage models. This role involves collaborating with Data Science and Analytics teams to productionize models and dashboards, continuously improving pipeline reliability by using Monte Carlo's product, and managing and creating data platform resources like database objects and AWS resources.
Provides end-to-end data observability solutions
Monte Carlo Data helps businesses ensure the reliability of their data through end-to-end data observability, allowing real-time monitoring of data freshness, volume, schema, and quality. Their platform includes tools for incident detection and resolution, which assist analysts in addressing data quality issues efficiently. By integrating with communication tools like Slack and JIRA, it fits seamlessly into existing data management processes. The goal is to help businesses avoid the costs associated with bad data, making it suitable for data-dependent companies across various industries.