Senior Data Engineer Data Quality & Systems Forensics

Current job opportunities are posted here as they become available.

Location:

Carson, CA (hybrid) or remote with strong Pacific Time overlap
Duration: 6 months, full-time
Compensation: $70K to $80K for the six-month engagement
Team:
Global Data & AI

Dermalogica is looking for a senior, hands-on data engineer to help us strengthen the fundamentals of our global data environment.

This is not a traditional reporting role, and it is not primarily a greenfield data engineering role. We are looking for someone who is exceptionally good at investigating how complex systems actually work, finding where data has gone wrong, fixing the underlying issue, and putting controls in place so it does not happen again.

You will inherit a prioritized backlog of known data quality and integration issues across a hybrid environment that includes ERP systems, e-commerce platforms, marketplace data, SQL Server databases, ETL processes, cloud data warehousing, and downstream analytics.

The right person will be comfortable peeling back the layers of an unfamiliar system: starting with a number that does not make sense, tracing it through databases, views, stored procedures, integration jobs and source systems, determining exactly where and why it broke, and driving the issue through to resolution.

We are looking for someone with strong technical depth, forensic instincts, practical judgment, and the confidence to operate independently in an environment where documentation is sometimes incomplete and the answer is not always obvious.

• Investigate and root-cause data discrepancies across source systems, databases, integrations, data warehouses, and reporting layers.
• Trace data end-to-end to understand where transformations, mappings, filters, jobs, or business rules are producing incorrect results.
• Own a prioritized backlog of data quality issues from investigation through remediation, validation, backfill, and closure.
• Audit existing SQL jobs, stored procedures, ETL processes, dependencies, and transformation logic to identify fragile or undocumented behavior.
• Identify and eliminate hardcoded business rules, silent failures, incomplete loads, duplicate data, and other recurring sources of data quality problems.
• Correct issues at the appropriate architectural layer rather than applying downstream patches.
• Recover and backfill missing or incorrect historical data and reconcile results back to source systems.
• Improve customer, channel, product, and other master-data mappings where inconsistent logic is affecting reporting.
• Replace fragile manual data processes with governed, scheduled, and monitored pipelines where appropriate.
• Build automated reconciliation, feed-health checks, and alerting so data failures are detected quickly rather than discovered through manual review.
• Improve dependency management, reload processes, and change controls so upstream changes do not create unexpected downstream issues.
• Document critical data lineage, system dependencies, transformation logic, and ownership as the environment is cleaned up.
• Work closely with internal IT, Finance, market teams, external development partners, and vendors to drive issues to resolution.
• Communicate technical findings clearly to both technical and non-technical stakeholders.
• 7+ years of hands-on data engineering, database engineering, or closely related experience in production environments.
• Deep experience with Microsoft SQL Server and T-SQL, including complex queries, views, stored procedures, scheduled jobs, and production troubleshooting.
• Strong understanding of ETL/ELT pipelines, system integrations, database dependencies, and data warehouse architecture.
• Demonstrated experience diagnosing and resolving data integrity or data quality incidents, not only building new…