Join BCG X, a leading technology and design unit, where you'll partner with consulting teams to deliver cutting-edge data and AI solutions. This role offers the chance to leverage advanced analytical methodologies and provide subject matter expertise in data engineering, driving accelerated execution support for BCG's consulting and practice areas. You will play a key role in developing and implementing innovative solutions that address complex client needs.

BCG X is a diverse group of over 3,000 tech experts dedicated to making a significant impact across various industries. We combine extensive experience and expertise to tackle society's biggest challenges, pushing the boundaries of what's possible. By fostering a stable ecosystem and leveraging BCG's global network, we empower talent to build groundbreaking businesses, products, and services from inception.

Collaborate with case teams to gather requirements, and subsequently design, develop, deliver, and support data and AI solutions tailored to client needs. Provide crucial technical support by deeply understanding relevant data management solutions and processes to construct high-quality, efficient analytics solutions.

Own the design, build, and operation of production-grade data platforms and pipelines across cloud, hybrid, and on-premise environments. Lead end-to-end data engineering delivery, encompassing ingestion, transformation, data modeling, orchestration, serving, and productionization for analytics and AI use cases. Architect scalable batch, streaming, real-time, and API-driven data solutions that are reliable, maintainable, and suitable for business-critical decisioning. Design trusted and reusable data foundations with robust practices for data quality, governance, cataloging, lineage, and access control.

A Bachelor's or Master's degree in Computer Science, Engineering, Technology, Data Science, or a related technical field is required, alongside 8+ years of experience in data engineering, data platforms, or enterprise AI/data solution delivery. Demonstrated experience leading production-grade data engineering programs, with hands-on involvement in architecture, implementation, code review, deployment, and operationalization, is essential.

Possess expert proficiency in at least one major programming language like Python, Java, or Scala. A strong understanding of distributed computing and modern data platforms, including Spark-based processing, performance tuning, and large-scale data system design, is critical. Deep experience with technologies such as Spark, DBT, Databricks, Snowflake, Hive, Hadoop, or equivalent big data and analytics ecosystems is expected. Significant cloud engineering experience across AWS, Azure, or GCP, with expertise in at least one cloud ecosystem, is also necessary.

Experience with GenAI implementation patterns, including RAG, retrieval pipelines, LLM application architecture, workflow design, evaluation, monitoring, and deployment is crucial. Ensure responsible use of AI tools across the SDLC to enhance engineering productivity while maintaining quality, security, and delivery accountability. Hands-on experience building trusted data foundations, including data modeling, pipeline productization, orchestration, CI/CD, containerization, and observability, is a must. Practical experience with data governance, metadata, cataloging, lineage, privacy, and security in enterprise data environments is vital.

Strong client-facing communication skills are needed, with the ability to influence senior stakeholders and effectively manage ambiguity, trade-offs, and difficult delivery conversations. Prior consulting experience or experience directly working with senior stakeholders on solution delivery is a plus. Experience in data-rich industries like Retail or Telecom with complex enterprise data landscapes, along with exposure to NoSQL, streaming, messaging, and event-driven architectures, would be beneficial. Familiarity with ML engineering or advanced analytics integration in production environments, and experience making pragmatic build-versus-buy recommendations, are also advantageous.