Data Engineer - CORO Products

CVs must be submitted in English to be considered

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

Bain's Next Generation Software Solutions (NGSS) team is responsible for discovering, building, scaling, supporting, and maintaining software products that enable Bain's intellectual property and consulting solutions.

Within this broader team, you'll be embedded in Coro, Bain's proprietary B2B go-to-market software suite. Coro delivers AI-accelerated data, analytics, and GTM tools — including MoneyMap, ProfitCube, Partner Sonar, and Coro Assist — that help revenue organizations identify opportunities, align commercial teams, and drive profitable growth at scale.

WHERE YOU'LL FIT WITHIN THE TEAM

As a Data Engineer, you'll be at the core of Coro's data platform, writing production Python that moves, transforms, and serves data across the business. This is a software engineering role: the work is designing, coding, testing, and operating Python services, libraries, and pipelines — not configuring tools or hand-writing queries. You will build systems that are readable, tested, observable, and cheap to change, working with analytics, engineering, and business teams to deliver reliable data products.

WHAT YOU'LL DO

Engineering & Solution Delivery (70%)

• Design, build, and maintain batch and streaming pipelines in Python, including the orchestration, scheduling, and monitoring around them.
• Write reusable, well-typed Python libraries and internal packages that other engineers and analysts build on.
• Build Python services and APIs that expose data to downstream applications, and integrate with third-party and internal APIs.
• Write unit, integration, and data-contract tests, and keep pipelines covered by automated CI.
• Profile and optimize Python code and data processing jobs for runtime, memory, and cost.
• Deploy and operate code in the cloud using containers, infrastructure as code, and CI/CD.
• Enforce access controls, secrets handling, and sensitive-data protections at every stage of the data lifecycle.

Continuous Improvement (30%)

• Find and fix efficiency, reliability, cost, and correctness problems in existing code and pipelines, and refactor toward simpler designs.
• Replace one-off scripts and notebooks with tested, packaged, scheduled code.
• Implement data quality checks, validation, and monitoring that catch issues before consumers do.

ABOUT YOU

Experience

• 2-4+ years of experience in the following areas:
• Shipping production Python, not just scripts or notebooks.
• Building and operating ETL/ELT pipelines at scale.
• Python data tooling: pandas, Polars, PyArrow, DuckDB, or PySpark.
• Schema enforcement and validation: Pydantic or similar.
• Workflow orchestration in code: Airflow, Dagster, Prefect, dbt, or Temporal.
• Testing and quality tooling: pytest, pyright, ty, pyrefly, ruff or black.
• Dependency and environment management: uv, Poetry, pip-tools, or conda.
• At least one public cloud (AWS, Azure, or GCP) and its Python SDK, with familiarity with the others.
• Cloud data platforms: Snowflake, Databricks, BigQuery, or Redshift.
• Relational and non-relational databases, including schema design.

Education

• Bachelor's degree or an equivalent combination of education, training and experience.

Knowledge, Skills & Abilities

• Strong, idiomatic Python: data structures, typing, error handling, generators/iterators, context managers, and the standard library.
• Software engineering fundamentals: modular design, dependency management, packaging, semantic versioning, and API design.
• Testing discipline: pytest, fixtures, mocking, and writing code that is testable by construction.
• Debugging and profiling; able to reason about performance, concurrency, and memory in Python.
• Working knowledge of SQL, data modeling, governance, and privacy practices sufficient to design sound schemas and query them effectively.
• Able to explain technical trade-offs to both technical and business audiences.

Additional Qualifications

• Python web/API frameworks (FastAPI, Flask).
• Async Python, multiprocessing, or other concurrency patterns.
• Streaming: Kafka, Kinesis, or Spark Structured Streaming.
• Infrastructure as code and automation (Terraform, GitHub Actions).
• Docker and Kubernetes.
• A second language: Go, Rust, Scala, or TypeScript.
• Generative AI, machine learning, or data science tooling.