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, a champion of diversity and a model of social responsibility. We are currently #1 ranked consulting firm on Glassdoor’s [1] Best Places to Work list and have maintained a spot in the top four on Glassdoor’s list since its founding in 2009. 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
Coro is Bain’s persistent product development and engineering organization serving the firm’s digital solutions across Commercial Excellence (B2B) and the newly established Demand Generation Suite (B2C). Coro brings together technology, data, and services to build and operate proprietary solutions that create differentiated value for Bain’s clients and case teams.
This role sits within the newly established B2C pillar of Coro, building the technical foundation for Artemis - Bain’s AI-driven marketing and commercial investment allocation platform, and one of three products in the Demand Generation Suite.
WHERE YOU’LL FIT WITHIN THE TEAM
The Senior Data Engineer owns the data ingestion, transformation, and quality infrastructure that forms the foundation of Artemis. The data layer is the first unlock for everything else on the product roadmap — nothing downstream in analytics, synthesis, or AI automation works without clean, well-structured client data. Senior Data Engineers build and maintain pipelines that take raw commercial datasets - media spend, impressions, sell-out point-of-sale data, household panel data (e.g., Kantar, Numerator), retailer electronic point-of-sale feeds, and campaign performance data and transform them into structured, reliable inputs for Artemis's Bayesian marketing mix models and channel allocation analytics.
This role requires both the engineering discipline to build production-grade pipelines and enough domain understanding to design data models that accurately represent the commercial and marketing data Artemis works with.
WHAT YOU’LL DO
Data Ingestion and Transformation
- Build and maintain pipelines that ingest, validate, and transform Artemis client datasets - including media platform feeds (spend, impressions), sell-out volumes, household panel data, and macro indicators — handling the full wrangling workflow from initial data diagnosis through mapping, transformation, and imputation to produce clean, model-ready outputs.
- Design and implement ETL pipelines that are reliable, testable, and adaptable to the variety of data formats and naming conventions encountered across different client engagements.
Data Quality
- Build the data quality assessment logic that validates completeness, consistency, format, and referential integrity across client datasets. Produce programmatic data quality reports that the consulting team and clients can act on.
- Ensure all data assets produced by Artemis pipelines conform to the shared Demand Generation Suite data model: structured, documented, and available via API to enable benchmarking and cross-engagement analytics.
Data Modeling and External Integrations
- Design and maintain adaptable data models that keep data structured, consistent, and accessible as business needs and the technology continue to evolve.
- Manage integrations with third-party and Bain-proprietary data sources used for enriching Artemis client datasets, including Nielsen, Circana, or GfK sell-out data, Kantar or Numerator panel data, media platform APIs, macro data feeds (weather, holidays, inflation), and digital shelf data.
- Work closely with AI Engineers to ensure the data layer produces outputs that are reliably structured for large language model-powered workflows.
Innovation & Emerging Technologies
- Drive innovation through out-of-the-box thinking to solve critical business challenges and demands.
- Participate in technical discovery, POCs, and innovation workstreams to validate new tools, technologies, and designs.
- Investigate and keep up-to-date on emerging data, AI, and agentic technologies and trends; lead knowledge sharing.
- Entrepreneurial spirit, willing to try new approaches.
ABOUT YOU
Technical
- Strong Python skills for data processing (pandas, polars, or equivalent), with experience in schema validation libraries (Pydantic, Great Expectations, or equivalent).
- Solid relational database experience: data modeling for analytical and transactional load, and query optimization.
- Experience ingesting and normalizing messy, unpredictable real-world datasets from heterogeneous sources such as ranging incomplete client data or unreliable 3rd party data sources in media, attribution etc.
- Data pipeline automation, including ingestion, transformation, load, valid. Hands-on experience with Azure Databricks, Delta Lake, and Spark-based pipeline development. Familiarity with Medallion Architecture and cloud data platforms (Azure, AWS, or GCP) is a strong advantage
- Working knowledge of cloud storage and data services (Azure preferred).
- Experience contributing to time and cost estimates of product enhancements, advise on trade-offs of features.
- Familiarity with AI coding tools (Cursor, Claude Code, Codex) to support and accelerate development workflows.
- Results-driven, with an analytical and collaborative mindset. Eager to learn and grow in a fast-paced Agile environment.
Education
- Associate/Bachelor's degree or an equivalent combination of education, training and experience.
Experience
- 3-5+ years of data engineering experience building ETL or data pipeline systems.
- Track record of delivering in an Agile environment.
- Experience with troubleshooting and issue resolution skills.
References
Visible links
1. https://www.bain.com/about/awards-recognition/?_gl=1*7at5cr*_gcl_au*MjM3NTAxNzIzLjE3NDA1MTAwMTk.*_ga*MTIxNzQzNDk4LjE3MzIxMjg3Mzg.*_ga_BS4G2KETDQ*MTc0MTAyNDg0Ny4zNy4xLjE3NDEwMjYyMTYuMC4wLjA.
