Senior Tech Lead – Ecommerce (Salesforce Commerce Cloud – SFCC B2C)
July 14, 2026Salesforce CPQ Architect
July 15, 2026
Experience: 8 Years
Budget: 1.2L-1.8L
JD:
Job Summary
We are seeking an experienced and highly skilled ETL Test Lead to spearhead our data quality and validation efforts. The ideal candidate will possess a strong background in ETL testing, data warehousing concepts, and test automation, with a particular emphasis on leveraging AI tools for ETL testing. This role requires a hands-on leader capable of defining test strategies, implementing automation frameworks, and mentoring a team of testers to ensure the integrity and reliability of our data pipelines.
Key Responsibilities
The ETL Test Lead will be instrumental in ensuring the accuracy, completeness, and performance of our data solutions. Key responsibilities include:
| Category | Responsibilities |
| ETL Testing Leadership | Lead the end-to-end ETL testing process, encompassing data extraction, transformation, loading, and validation across diverse systems. Define and implement comprehensive ETL test strategies, frameworks, and automation approaches. |
| Data Validation & Quality | Perform rigorous data validation, reconciliation, and integrity checks across source, staging, and target systems. Ensure data quality, consistency, and compliance with both business and regulatory requirements. |
| Test Automation & AI Adoption | Drive the adoption of test automation for data validation using modern frameworks such as Python, PyTest, Great Expectations, and dbt tests. Leverage AI-driven testing tools for anomaly detection, automated test case generation, and intelligent data validation, with mandatory hands-on experience in AI-enablement of testing (e.g., using GitHub Copilot for test script creation). |
| Team Leadership & Collaboration | Lead and mentor a team of ETL testers, ensuring timely delivery within established quality standards. Collaborate effectively with data engineers, BI developers, and business stakeholders to understand data requirements and refine test coverage. |
| Defect Management & CI/CD | Manage the defect lifecycle, conduct risk assessments, and oversee regression testing. Integrate ETL test automation into CI/CD pipelines for continuous validation of data workflows. Provide comprehensive reporting and insights on test execution, data quality metrics, and coverage to leadership. |
Required Skills and Experience
Candidates for this role should demonstrate a robust technical skill set and proven experience in the following areas:
| Skill Category | Description and Experience |
| Total Experience | 3+ years in ETL testing, with significant experience in a lead role. |
| AI Tools for ETL Testing | 1–2 years of mandatory hands-on experience with AI-based anomaly detection, automated test script creation, and AI-enablement of testing (e.g., GitHub Copilot). |
| SQL Expertise | 4–6 years with advanced SQL queries, joins, and performance tuning across various databases (Oracle, MS SQL Server, PostgreSQL, MySQL). |
| ETL Tools | 4–6 years with major ETL tools (Informatica, Talend, SSIS, DataStage, etc.), including deep understanding of transformation logic and process validation. |
| Python & Automation | 2+ years in Python for test automation scripting and data reconciliation. 2–3 years in Test Automation for Data Validation (Python, PyTest, Great Expectations, dbt tests). |
| Databricks | 2–3 years with Databricks (PySpark, Spark SQL, Delta Lake, Notebooks) for ETL testing, reconciliation, and automation. |
| Cloud Data Platforms | 2+ years with cloud-native ETL validation on platforms like AWS Redshift, Azure Synapse, GCP BigQuery, and Snowflake. |
| Data Warehousing Concepts | 4+ years with fact/dimension validation and star & snowflake schema testing. |
| Test Management & CI/CD | 3+ years with Test Management Tools (JIRA, TestRail, ALM, Zephyr) for test planning, execution, and defect management. 2–3 years with Version Control & CI/CD (Git, Jenkins, GitHub Actions, Azure DevOps) for automation integration. |
| BI / Visualization Tools | 1–2 years with BI / Visualization Tools (Tableau, Power BI, QlikView) for report/dashboard validation. |
Nice-to-Have Skills
•Big Data Ecosystem: 2+ years with Hadoop, Hive, Spark, Impala for large dataset validation and querying.
•Data Orchestration Tools: Experience with Airflow, Azure Data Factory, Control-M for workflow validation and scheduling.
•Data Quality & Governance Tools: Familiarity with Collibra, Informatica Data Quality, Talend DQ for data quality rules and profiling.
•Machine Learning / AI Integration: Basic knowledge of ML models for anomaly detection in large datasets.
•DataOps Practices: Exposure to continuous testing, monitoring, and observability for data pipelines.
