Engineer, TSG Software Quality Assurance
Description & Requirements
COMPANY PROFILE
Bain & Company is one of the top management consulting firms in the world that helps the world’s most ambitious changemakers define the future.
Across 65 cities in 40 countries, we work alongside our clients as one team with a shared ambition to achieve extraordinary results, outperforming the competition, and redefine industries. We complement our tailored, integrated expertise with a vibrant ecosystem of digital innovators to deliver better, faster, and more enduring outcomes.
The firm established several functions in the Indian market early 2000s and its remit across functions has expanded over time. Since 2019, these functions have become part of Global Business Services (GBS). Global Business Services (GBS) is a network of five interconnected business functions hubs across India, Poland, Malaysia, Mexico and Portugal, serving Bain globally to run our business, support other functions, and help drive innovation internally. We are over 1000 business professionals – serving functions in operations, HR, finance, legal, tech, marketing, research, and data analytics – who support our offices globally. Our mantra of “shared innovation, seamless execution,” underpinned by a passion for results, teamwork, and creativity, helps Bain stay at the top of our game operationally.
WHAT MAKES US A GREAT PLACE TO WORK
We are proud to be consistently recognized as one of the world’s best places to work. We are currently the top-ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times.
Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.
WHO YOU’LL WORK WITH
This position sits within an agile data engineering team, owning the quality of end-to-end data pipelines from source ingestion through medallion-layer transformation (Bronze/Silver/Gold) to BI/reporting consumption. The role suits someone who thinks like a data architect and builds/tests like an engineer, equally comfortable with pipeline design and hands-on SQL, PySpark, and automated data quality frameworks.
WHERE YOU’LL FIT WITHIN THE TEAM
The engineer independently owns the data testing strategy for their workstream, including a reusable SQL validation library usable for both manual and automated checks, and constructively challenges design or process decisions that risk data integrity or delivery quality. Works closely with Data Engineers, the QA Manager, Product/Project Managers, and business stakeholders.
WHAT YOU’LL DO
Requirement, Project and Business Understanding (20%)
- Develop deep expertise in the data domain and business context source systems, transformation logic, medallion layers, and downstream BI consumption.
- Build a thorough understanding of the pipeline lifecycle (ingestion → staging → transformation → curated/BI layer).
- Review source-to-target mappings and transformation logic before test design begins.
- Support the team on data QA methodologies and processes, with guidance from QA leadership.
Test Planning, Design, Execution, and Defect Management (35%)
- Own the data testing strategy across full, incremental, delta, and historical loads, including insert/update/delete/cancel and effective-dated transactions.
- Design and execute ETL/ELT validation, reconciliation, schema, null/completeness, and transformation test cases across the medallion architecture.
- Validate pipelines end-to-end source through Databricks/ADF/Spark to final BI output, confirming numbers, aggregations, and business logic tie out.
- Define and enforce data quality rules and build reusable checks; investigate anomalies and drive root-cause analysis with Data Engineers.
- Manage the defect lifecycle and prepare test evidence, quality dashboards, and RCA reporting for release sign-off.
SQL Script & Automation Engineering (20%)
- Build and maintain a structured, version-controlled Databricks/SQL validation queries usable standalone for manual checks and pluggable into automated regression/CI without rework.
- Own inline maintenance of the library: refactor as schemas evolve, retire obsolete checks, keep naming and parameterization consistent, and document each script's purpose, inputs, expected output, and edge cases so it stays usable by the wider team.
- Set up and maintain automated data testing frameworks (e.g., Datagaps, PySpark-based checks) that consume the library for scheduled/CI-triggered runs.
- Work with Data Engineers to wire validation into CI/CD, ensuring failures surface with actionable, traceable output.
Collaboration (15%)
- Provide input into the test strategy mix (manual/automated/framework-driven) in partnership with QA leadership and data engineering.
- Constructively challenge decisions that risk data quality or testability and propose practical alternatives.
- Participate fully in agile ceremonies; raise risks and quality concerns early and clearly.
- Partner with the business to understand the wider data system and act as a quality partner for delivery.
Leadership (10%)
- Own data testing deliverables, including the health of the automation/SQL library, for the workstream.
- Lead or contribute to QA initiatives that raise the bar on data testing practice across the team.
- Mentor junior testers on ETL/data testing techniques, SQL standards, and automation tooling
ABOUT YOU
- Strong understanding of ETL/ELT concepts, dimensional modeling, fact/dimension tables, and medallion architecture (Bronze/Silver/Gold), with the ability to trace a data point end-to-end into its BI report representation.
- Hands-on proficiency with Azure SQL/Databricks/Apache Spark (PySpark) for large-scale data validation and transformation testing.
- Working knowledge of Azure Data Factory (pipelines, linked services), Azure Storage (partitioning, Parquet/Delta), and third-party API integrations (e.g., Salesforce, etc.)
- Advanced SQL for backend validation and reconciliation across SQL Server/PostgreSQL/Databricks, with emphasis on writing maintainable, reusable, parameterized scripts.
- Working knowledge of Python for test scripting and validation automation.
- Practical exposure to ETL/data quality tools, Datagaps DataOps Suite (good to have), or equivalent (DQX, QuerySurge, Informatica, Talend, DataStage, SSIS).
- Ability to define and apply data quality rules systematically (completeness, accuracy, consistency, uniqueness, timeliness).
- Hands-on experience with AI-assisted test generation or anomaly detection, with the ability to adapt checks for AI-produced and consumed data pipelines.
- Comfort with Agile/Kanban delivery, CI/CD practices, and defect management tooling (JIRA, Azure Boards, or similar).
- A curious, quality-first mindset confident to constructively challenge decisions when quality is at risk.
- Strong written/verbal communication and stakeholder partnership skills; strong critical thinking and independent problem-solving.
Experience:
- 4-6 years in data testing/automation, with hands-on ETL, Data Warehouse, and/or Big Data testing experience.
- Proven experience testing pipelines on Databricks/Spark within a medallion or layered Lakehouse architecture.
- Experience with Azure Data Factory and Azure cloud storage constructs.
- Experience validating data through to BI/reporting layers (Power BI, Tableau, or similar), not just the warehouse layer.
- Experience with data quality frameworks/rule design and writing SQL validation scripts reused across both manual testing and automated regression, ideally with exposure to a tool such as Datagaps.
- Experience with SDLC/STLC, Agile ceremonies, and defect lifecycle management; prior ownership of a test strategy independently is strongly preferred.
- Familiarity with AI-assisted testing or data validation tools