Senior Data Engineer (Contract) - Data Quality & Systems Forensics

Dermalogica•Carson, CA

14h•Hybrid

About The Position

Dermalogica is seeking a senior, hands-on data engineer to strengthen the fundamentals of its global data environment. This role focuses on investigating complex systems, identifying and fixing data quality issues, and implementing controls to prevent recurrence. The engineer will address a backlog of known data quality and integration issues across a hybrid environment including ERP systems, e-commerce platforms, marketplace data, SQL Server databases, ETL processes, cloud data warehousing, and downstream analytics. The ideal candidate is comfortable dissecting unfamiliar systems, tracing data from source to resolution, and possesses strong technical depth, forensic instincts, practical judgment, and the ability to work independently in an environment with sometimes incomplete documentation.

Requirements

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 pipelines.
Ability to take an ambiguous problem (e.g., "these numbers do not reconcile") and independently determine where, when, and why the problem occurred.
Experience tracing data across multiple systems and determining the appropriate source of truth.
Strong analytical and forensic problem-solving skills.
Comfortable digging through unfamiliar systems, testing assumptions, and following evidence until the root cause is understood.
Strong practical judgment and common sense.
Ability to make safe changes in shared production environments and understand downstream impacts before implementing them.
Ability to work independently with limited direction and drive issues across multiple teams through completion.
Clear written and verbal communication skills.

Nice To Haves

Experience with BigQuery or another modern cloud data warehouse.
Experience with SSIS, SQL Agent, or similar scheduling/orchestration technologies.
Experience working with ERP data such as JD Edwards, Microsoft Dynamics NAV, or Business Central.
Familiarity with commerce platforms such as Shopify or Amazon marketplace data.
Experience working with financial or commercial datasets, including invoices, transactions, sales, returns, tax, accruals, or general-ledger data.
Experience reconciling data between operational systems, financial systems, warehouses, and BI/reporting tools.
Experience with data observability, automated reconciliation, data-quality monitoring, or building similar controls.
Experience working with legacy systems, offshore development teams, external vendors, or environments where system knowledge is distributed across multiple groups.

Responsibilities

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.