Senior Associate Data Engineer

• Write and optimize SQL-based queries and transformations to support metric calculations, data checks, and deliverables for Bain case teams and external customers.

• Operationalize the annual benchmarking cycle: build cohorts from defined selection criteria, calculate benchmarks and quartiles, validate the output, and ensure it is published to the platform.

• Design and execute data cleaning rules that improve structural validity across the benchmark database.

• Scope, design, and build platform features that strengthen the data pipeline, such as validation tooling that flags taxonomy breaks and percentile performance views across the database.

• Make schema and architecture decisions across the ingestion, metric calculation, and benchmarking pipeline, ensuring solutions are scalable, automated, and maintainable.

• Gather business requirements and process knowledge from Strategy & Ops, and translate them into scalable data models, transformations, and outputs aligned to end-user needs.

• Investigate data issues within the pipeline, identify root causes, communicate implications to stakeholders, and propose effective solutions.

• Prepare clear technical and process documentation for ongoing maintenance, knowledge transfer, and future reference.

• Manage communication with onshore teams and stakeholders regarding project progress, data quality, and key insights.

• Support project planning and resource management, including prioritization, work allocation, and tracking progress across concurrent initiatives.

• Provide technical guidance and day-to-day support to junior team members, review output for quality, and drive reusable capability building through shared assets, frameworks, and documentation standards.

Qualifications and Experience

• Work Experience: 6+ years of experience in Data Engineering, Data Management, Business Intelligence, or Business Analytics, with strong hands-on experience in SQL, Python, data warehousing, and relational database design.

• Education Required: Incumbent should be a graduate/postgraduate from a top-tier institute or have pursued a statistical/analytical course from a Tier 1 university.

• Preferred: Concentration in a quantitative discipline such as Engineering, Computer Science, Econometrics, Business Analytics, or Market Research is strongly preferred.

Knowledge, Skills and Abilities Required

• Advanced SQL knowledge (complex queries, stored procedures, performance tuning, optimization, etc.).

• Strong hands-on Python for data processing, validation, automation, and scripting.

• Strong understanding of ETL concepts, data warehousing principles, and relational database architecture, with hands-on experience on SQL Server or a comparable relational database.

• Working knowledge of cloud technologies, preferably AWS (e.g., EC2, RDS, CloudWatch).

• Strong understanding of data modeling, schema design, and scalable data pipeline architecture.

• Ability to independently scope features, design solutions, and make architecture decisions, including where requirements start out ambiguous.

• Experience with Git-based version control and CI/CD workflows; GitLab experience preferred.

• Experience coordinating projects, managing resources, and driving execution across multiple stakeholders.

• Ability to provide technical leadership, coach junior team members, and review output for quality and accuracy.

• Strong organizational skills, including planning, prioritization, and time management.

• High attention to detail and commitment to data quality.

• Passion for solving complex data problems and working with large, diverse data sets.

• Ability to quickly learn new tools, technologies, and business contexts.

• Good to have (not mandatory): SaaS domain expertise or P&L fluency, and prior experience with benchmarking or survey-based data sets.

  • Good to have (not mandatory): Familiarity with PHP or JavaScript/React for full-stack contribution, and exposure to LLM/API integration.