Specialist - Data Engineering

Your opportunity

Schwab Description

At Charles Schwab, our purpose is simple: we champion client’s goals with passion and integrity. Guided by honesty, mutual respect and a commitment to doing what’s right, we bring innovation, education, and service together to help shape financial futures. Our people are the foundation of our success – they approach their work with curiosity and collaboration, coming together to create solutions that make a meaningful impact for clients and communities. As we expand into India, we are bringing this same culture of inclusion, learning, and opportunity to new talent. Joining us means becoming part of a global team where your work matters and your future can take shape.

Schwab India Description

Our Hyderabad location is central to Schwab’s growth, bringing together talented people and technology to drive innovation, scale and efficiency. Here, you will work alongside teams who create solutions that support millions of clients every day. The work you do is more than daily operations – it’s a chance to experiment, learn, and build within a values-driven, supportive environment. This is a unique opportunity to be part of our early growth phase and shape something new, backed by the stability and strength of a Fortune 500 company. Your impact begins on day one, and your contributions will help define our future in the region.

Role Description

The Specialist, Data Engineering role is a hands-on QA tester / data engineer position within Schwab Data India focused on validating, testing, and supporting data engineering solutions that enable enterprise-wide analytics and operational reporting. This role partners closely with product owners, developers, and quality engineering teams to ensure reliable, scalable, and well-governed data pipelines and platform capabilities aligned with Schwab standards.

As part of the OneSchwab/OneGCC model, this QA tester / data engineer contributes to high-quality delivery through strong testing practices, data validation, automation, defect analysis, operational discipline, and close collaboration across global teams. The role is ideal for a technologist who enjoys ensuring data quality and delivery reliability in a fast-paced, regulated enterprise environment.

What you have

Key Responsibilities

• Requirements & Design Review: Review business requirements, source-to-target mappings, and technical design documents to identify validation needs, test coverage gaps, and quality risks, and provide timely engineering feedback.
• Test Planning & Scenario Coverage: Review requirements, source-to-target mappings, and technical designs to identify test scenarios, edge cases, validation rules, and quality risks.
• Data Testing & Validation: Write and execute SQL-based data validations, reconcile source and target data, validate transformation logic, support functional and regression testing, and help resolve defects through root-cause analysis.
• Test Automation: Develop, maintain, and enhance automation scripts and reusable test assets to improve validation coverage, repeatability, and release confidence across data engineering workflows.
• AI-Assisted Automation Development: Use AI-assisted tools and techniques responsibly to accelerate development of automation scripts, improve test design efficiency, and strengthen validation productivity while adhering to enterprise engineering and control standards.
• Defect & Release Support: Coordinate with cross-functional teams during test cycles and releases, track defects, validate fixes, and ensure timely completion of assigned testing and quality assurance activities.
• Documentation & Test Standards: Maintain test documentation, validation evidence, reusable QA assets, and engineering artifacts in line with Schwab delivery and control standards.

Required Qualifications

Ideal Candidate Profile

• 3+ years of overall experience with strong hands-on expertise in data engineering testing, ETL validation, QA automation, or data warehouse environments.
• Experience working on large-scale data testing or data engineering quality assurance initiatives with strong hands-on ownership of automation development in an Agile delivery model.
• Strong foundation in data validation, defect identification, troubleshooting, and quality engineering best practices across testing lifecycles.
• Ability to work collaboratively across product, development, and QA teams in a fast-paced enterprise setting.

Required Experience

• Hands-on experience in Data Warehouse and ETL testing, validation, or quality engineering, with demonstrated ability to develop automation scripts for test execution, reconciliation, and validation, preferably using IICS or PowerCenter.
• Experience with Google Cloud Platform (GCP) and modern cloud-based data engineering practices.
• Proficiency in SQL, Python, and basic Unix/shell scripting to develop automation scripts and support testing, validation, automation, and troubleshooting.
• Experience using AI-assisted engineering tools to help create, refine, and optimize automation scripts and testing accelerators while applying sound engineering judgment, validation, and governance.
• Experience with Agile delivery tools such as Jira or Rally, with a solid understanding of software development lifecycle practices.
• Strong analytical, troubleshooting, defect diagnosis, test evidence review, and root-cause analysis skills.
• Ability to manage priorities independently, communicate clearly, and deliver high-quality work within tight timelines.

Preferred Qualifications

• Working knowledge of CI/CD and DevOps tools such as Bamboo, Bitbucket, GitHub, or GitHub Actions.

Job Sub-Family Specific Competencies

• Analytical Thinking – Approaching a problem by using a logical, systematic, sequential approach.
• Attention To Communication – Ensuring that information is passed on to others who should be kept informed.
• Data Engineering Fundamentals – Strategically managing and operationalizing data pipelines and storage systems for data mining, analytics, and machine learning, prioritizing compliance, security, and scalability.
• Personal Credibility – Demonstrated concern that one be perceived as responsible, reliable, and trustworthy.
• Results Orientation – Focusing on the desired end result of one’s own or one’s unit’s work; setting challenging goals, focusing effort on the goals, and meeting or exceeding them.
• Thoroughness – Ensuring that one’s own and others’ work and information are complete and accurate; careful preparation for meetings and presentations; following up with others to ensure agreements and commitments have been fulfilled.