Data Engineer II - (Remote - US)

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Requirements:

• 3+ years of experience in a data engineering role.
• Strong Python skills (Understands tradeoffs, optimization, etc).
• Strong SQL skills (CTEs, window functions, optimization).
• Experience working in cloud environments (AWS preferred, GCS, Azure).
• An understanding of how to best structure data to enable internal and external facing analytics.
• Familiarity with calling APIs to retrieve data (Authentication flows, filters, limits, pagination).
• Experience working with DevOps to deploy, scale and monitor data infrastructure.
• Scheduler experience either traditional or DAG based.
• Comfortable working with multi-TB cloud data warehouses (Snowflake preferred, Redshift, Big Query).
• Experience with other DBMS systems (Postgres in particular).
• Nice to haves: experience with web analysis such as creating data structure that supports product funnels, user behavior, and decision path analysis.
• Understanding of Snowflake external stages, file formats and snowpipe.
• Experience with orchestration tools particularly across different technologies and stacks.
• Experience with dbt.
• Knowledge of Ad Tech, Google Ad Manager and all its fun quirks.
• Familiarity with event tracking systems (NewRelic, Snowplow, etc).
• Experience with one or more major BI tools (Domo, Looker, PowerBI, etc).

Responsibilities:

• Create data pipelines that make data available for analytic and application use cases.
• Develop self-healing, resilient processes that do not require constant care and feeding to run smoothly.
• Create meaningful data quality notifications with clear actions for interested parties including other internal teams and other members of the data and analytics team.
• Leading projects from a technical standpoint, creating project Technical Design Documents.
• Support data analysts and analytics engineers ability to meet the needs of the organization.
• Participate in code reviews, understanding coding standards, ensuring test coverage and being aware of best practices.
• Build or implement tooling around data quality, governance and lineage, in the dbt framework and Snowflake but external to that as needed.
• Provide next level support when data issues are discovered and communicated by the data analysts.
• Work with data analysts and analytics engineers to standardize transformation logic in the dbt layer for consistency and ease of exploration by end users.
• Enable analytics engineers and data analysts by providing data modeling guidance, query optimization and aggregation advice.