Data Backfill Audit
2 months ago
Why do we need this role?
In Q4 2021, Chegg performed a historical data backfill of Adobe logs. Site activity from our legacy Adobe
logs was transformed and migrated into the structure of our Chegg-owned event schema. During the
project, key metrics were validated to ensure that the transformed data aligned with historical metrics.
Over the last four months, as more reports cut over to the new data source, we’ve discovered issues in
the secondary and tertiary metrics that were never validated during the backfill. Reacting to these
issues is 1) causing data users to become less trustful of data and 2) sucking up bandwidth from teams
who are already committed to H1 and H2 roadmap programs.
What will this role deliver?
This role will perform an audit of our event data to identify data issues and work with the Data Engineer
Contractor to resolve them. The initial focus will be on validating secondary and tertiary metrics from
the historical. The audit and reprocessed data will deliver a purified event lake that is accurate and
trusted.
Responsibilities
This role will identify issues in the historical backfill, determine the root cause of issues, determine the
best method to resolve the issue and write the data spec that instructs Data Engineering how to resolve
the issue. Key to this role will be having the ability to formulate complex data queries, construct and
test hypotheses as to what might’ve caused a data issue, then partner with engineering to resolve the
gaps.
Requirements
• 3-7 years of demonstrated success in an analytics role developing and analyzing data processing
frameworks
• Top-decile data wrangling skills; strength in SQL and Python for data analysis and transformations
• Experience with Adobe Analytics Clickstream logs preferred
• Experience with data models and database technologies, especially Redshift and Spark
• Experience with Databricks preferred
• Experience troubleshooting and resolving data quality issues
• Able to communicate clearly, especially with written documentation
• Self-motivated, independent, organized and proactive, highly responsive, flexible and adaptable
when working across teams
• Bachelor’s degree in a quantitative field (e.g. engineering, sciences, math, statistics, business or
economics) required. Master’s degree in a quantitative field preferred
• Passionate about producing high-quality data products and communicating recommendations
Other Details
Keywords : .
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