SDET / Test Engineering Lead

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. Must not include Job ID etc. Must preserve all substantive info, no invention. Remove boilerplate (EEO, legal, etc). Must not include filler like "commensurate with experience". Must not create Pay/Schedule/Benefits unless posting actually states them. The posting includes Job Expectations: not eligible for Visa sponsorship, hybrid work schedule, must be able to work on-site at any listed locations, relocation assistance not available. That's about schedule/location, maybe schedule? It mentions hybrid work schedule, so we can include a Schedule section with that info. Benefits? Not mentioned. Pay? Not mentioned. So we can include About the role (the initial paragraph), Responsibilities (the "In This Role, You Will" bullet list), Requirements (maybe Required Qualifications), Qualifications (Desired Qualifications), Skills (maybe combine some?), but we need to follow headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We should only include sections with content. We have content for About the role, Responsibilities, Requirements (Required Qualifications), Qualifications (Desired Qualifications), Skills (maybe we can extract skill items from Required/Desired? But we might not need Skills if we can put skill details under Requirements/Qualifications. However we need to decide: The instruction says prefer a few substantive sections over many thin ones. Could combine Required and Desired into Requirements and Qualifications. Skills maybe not needed if no explicit skills list separate. But we could still include Skills if we can pull out skill-related items like "test automation using Python and/or Java", "Apache Spark", "SQL-based data validation", etc. However those are already in qualifications. Might be redundant. Safer to include only sections we have clear content: About the role, Responsibilities, Requirements, Qualifications, Schedule. Benefits and Pay omitted because not present. Skills omitted because not explicit separate list. Ensure we don't create empty sections. Now format: Use

for each section heading. Use

• for any list of duties/requirements/benefits. Use

  for prose. We need to preserve all substantive information. Let's extract: About This Role: paragraph(s) before "In This Role, You Will". That includes: "Wells Fargo is seeking an SDET/Test Engineering Lead to join the Shared Service Operations Technology (SSOT) team and lead quality engineering, test automation, and data validation efforts for the SSOADS platform. Our mission is to deliver reliable, scalable, and secure cloud-native data platforms that empower Shared Services Operations teams to make timely, accurate, and data-driven decisions. This role will be responsible for defining and executing the quality strategy across enterprise data products, with a focus on data pipeline testing, automated test frameworks, data quality assurance, and end-to-end validation of cloud-based data solutions. The ideal candidate will bring extensive experience leading quality engineering initiatives for modern data platforms, including data lakes, data warehouses, ETL/ELT pipelines, big data ecosystems, and cloud-native analytics environments. This individual will establish testing standards and automation frameworks while leading a team responsible for validating data ingestion, transformation, reconciliation, conformance, reporting, and regulatory data delivery processes. The role will partner closely with engineering, architecture, and product teams to ensure the integrity, accuracy, and reliability of critical enterprise data assets." That's the About the role content. Responsibilities: The bullet list after "In This Role, You Will". Each line starts with a verb. We need to capture each as list item. List items: - Lead quality engineering strategy and governance for enterprise-scale data platforms and data pipelines. - Define and implement comprehensive testing frameworks for batch, streaming, and event-driven data processing solutions. - Establish automation standards for data validation, reconciliation, regression testing, and data quality certification. - Design and oversee testing across ingestion, conformance, curated, and reporting layers. - Develop automated validation solutions for BigQuery, GCS, Dataproc, Spark, Cloud Composer, Pub/Sub, and related GCP services. - Define and implement Data Quality (DQ) testing practices including completeness, accuracy, consistency, timeliness, uniqueness, and reconciliation controls. - Drive shift-left testing practices within Agile delivery teams. - Lead test planning, test execution, defect triage, and release certification activities. - Partner with Data Engineers, Architects, Product Owners, and Business SMEs to ensure data requirements are testable and measurable. - Establish CI/CD-integrated automated testing and quality gates for data pipelines. - Develop strategies for performance, scalability, and resiliency testing of data processing workloads. - Ensure compliance with regulatory, audit, governance, and data lineage requirements. - Define and track quality metrics, test coverage, defect leakage, and release readiness KPIs. - Mentor QA engineers, SDETs, and data testers while promoting automation-first testing culture. - Collaborate with production support teams to identify root causes of data issues and implement preventative quality controls. - Evaluate and implement modern testing tools and frameworks to improve efficiency and reduce operational risk. That's responsibilities. Requirements: Required Qualifications list. It includes multiple bullet-like sentences each starting with a number? Actually they are separate lines each starting with a number+ years. We'll treat each as list item. Required Qualifications items: - 5+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education - 5+ years of experience in Quality Engineering, Data Testing, or Test Automation. - 5+ years of experience leading QA teams or test strategy initiatives. - 5+ years of experience testing ETL/ELT pipelines, Data Warehouses, Data Lakes, and Big Data platforms. - 5+ years' experience validating large-scale datasets in BigQuery, Teradata, Oracle, SQL Server, or similar platforms. - 5+ years of experience with test automation using Python and/or Java. - 2+ years of experience working with Apache Spark and distributed data processing validation. Qualifications: Desired Qualifications list. Items: - Experience integrating automated testing into CI/CD pipelines. - Experience building automated data validation and regression testing frameworks. - Knowledge of data quality testing methodologies and controls. - Hands-on experience with SQL-based data validation and reconciliation testing. - Strong understanding of Agile, DevOps, and Shift-Left testing practices. - Experience creating test strategies, test plans, and quality metrics. - Excellent stakeholder management and communication skills. - Experience testing cloud-native data platforms on GCP. - Knowledge of Apache Iceberg, Lakehouse architectures, and BigQuery external tables. - Experience validating streaming pipelines and event-based architectures. - Regulatory reporting, financial crimes, AML, Fraud, KYC, or compliance domain experience. - Experience designing automated reconciliation frameworks across source-to-target data flows. - Knowledge of metadata management, data lineage, and data governance concepts. - Experience with performance and volume testing for large-scale data processing systems. - Experience working with containerized workloads and Kubernetes. - Experience supporting audit and regulatory validation activities. - Strong leadership experience managing offshore/onshore QA teams. Schedule: Job Expectations: This position is not eligible for Visa sponsorship, This position offers a hybrid work schedule, Must be able to work on-site at any of the listed locations, Relocation assistance is not available for this position. Also includes posting locations and end date but those are minor metadata; we can drop or include in short p. The instruction: do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

  or drop it. So we can include a short paragraph with location and posting end date maybe. But we should not create a Schedule section unless posting actually states them. The schedule info is present: hybrid work schedule, must be able to work on-site at any listed locations, relocation assistance not available, not eligible for Visa sponsorship. That's schedule/location/visa. We'll put that in Schedule section. We should not include "Posting End Date: 17 Sep 2026" maybe as minor metadata; we can include in a short p under Schedule or maybe drop. The instruction: fold minor metadata into a short

  or drop it. So we can include a short p with location and end date. Thus Schedule section:

  Schedule

  then

  Hybrid work schedule; must be able to work on-site at any of the listed locations (401 Las Colinas Blvd W Bldg A, Irving, Texas 75039); not eligible for Visa sponsorship; relocation assistance not available.

  Possibly also include posting end date? Could add but it's minor. We'll include maybe after location. Now ensure we don't repeat job title. No mention of Wells Fargo? It's okay to keep company name in About the role. Now output HTML fragment only. Let's craft:

  About the role

  Wells Fargo is seeking an SDET/Test Engineering Lead to join the Shared Service Operations Technology (SSOT) team and lead quality engineering, test automation, and data validation efforts for the SSOADS platform. Our mission is to deliver reliable, scalable, and secure cloud-native data platforms that empower Shared Services Operations teams to make timely, accurate, and data-driven decisions. This role will be responsible for defining and executing the quality strategy across enterprise data products, with a focus on data pipeline testing, automated test frameworks, data quality assurance, and end-to-end validation of cloud-based data solutions. The ideal candidate will bring extensive experience leading quality engineering initiatives for modern data platforms, including data lakes, data warehouses, ETL/ELT pipelines, big data ecosystems, and cloud-native analytics environments. This individual will establish testing standards and automation frameworks while leading a team responsible for validating data ingestion, transformation, reconciliation, conformance, reporting, and regulatory data delivery processes. The role will partner closely with engineering, architecture, and product teams to ensure the integrity, accuracy, and reliability of critical enterprise data assets.

  Responsibilities

  • Lead quality engineering strategy and governance for enterprise-scale data platforms and data pipelines.
  • Define and implement comprehensive testing frameworks for batch, streaming, and event-driven data processing solutions.
  • Establish automation standards for data validation, reconciliation, regression testing, and data quality certification.
  • Design and oversee testing across ingestion, conformance, curated, and reporting layers.
  • Develop automated validation solutions for BigQuery, GCS, Dataproc, Spark, Cloud Composer, Pub/Sub, and related GCP services.
  • Define and implement Data Quality (DQ) testing practices including completeness, accuracy, consistency, timeliness, uniqueness, and reconciliation controls.
  • Drive shift-left testing practices within Agile delivery teams.
  • Lead test planning, test execution, defect triage, and release certification activities.
  • Partner with Data Engineers, Architects, Product Owners, and Business SMEs to ensure data requirements are testable and measurable.
  • Establish CI/CD-integrated automated testing and quality gates for data pipelines.
  • Develop strategies for performance, scalability, and resiliency testing of data processing workloads.
  • Ensure compliance with regulatory, audit, governance, and data lineage requirements.
  • Define and track quality metrics, test coverage, defect leakage, and release readiness KPIs.
  • Mentor QA engineers, SDETs, and data testers while promoting automation-first testing culture.
  • Collaborate with production support teams to identify root causes of data issues and implement preventative quality controls.
  • Evaluate and implement modern testing tools and frameworks to improve efficiency and reduce operational risk.

  Requirements

  • 5+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
  • 5+ years of experience in Quality Engineering, Data