Senior Data Engineer (Contract) - Data Quality & Systems Forensics at Dermalogica in San Francisco, CA

Job description

• 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.

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.

Requirements

• Must Have
• 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 — for example, “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. You are comfortable digging through unfamiliar systems, testing assumptions, and following evidence until you understand the root cause.
• Strong practical judgment and common sense. You know when something technically works but does not make sense from a business or data perspective.
• 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.
• 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.

Skills

Required

• Microsoft SQL Server
• T-SQL
• ETL/ELT pipelines
• System integrations
• Database dependencies
• Data warehouse architecture
• Root cause analysis
• Data tracing across systems
• Analytical and forensic problem-solving
• Practical judgment
• Safe changes in production environments
• Independent work

Preferred

• BigQuery
• SSIS
• SQL Agent
• Financial/commercial datasets
• Data observability
• Automated reconciliation
• Data-quality monitoring
• Legacy systems

Benefits

• The total expected compensation for this 6 month contract will range from $70K to $80K. The exact amount is determined by various factors including experience, skills, education, location, and budget.