Overview

Boston Consulting Group partners with leaders in business and society to tackle their most important challenges and capture their greatest opportunities.

Responsibilities

• Owning the design, build, and operation of production-grade data platforms and pipelines across cloud, hybrid, and on-premise environments.
• Leading end-to-end data engineering delivery across ingestion, transformation, data modeling, orchestration, serving and productionization for analytics and AI use cases.
• Architecting scalable batch, streaming, real-time, and API-driven data solutions that are reliable, maintainable, and fit for business-critical decisioning.
• Designing data foundations that are trusted and reusable with strong practices across data quality, governance, cataloging, lineage, and access control.
• Leading GenAI-enabled implementations with strong architectural judgment and hands-on credibility across RAG, retrieval design, LLM application patterns, workflow design, evaluation and production deployment.
• Writing, reviewing and raising the bar on engineering work in Python, Java, or Scala, with strong depth in distributed data systems and modern data platforms.
• Building reusable data engineering assets and implementation patterns that accelerate delivery across client engagements.
• Translating ambiguous business problems into scalable data and AI engineering solutions in partnership with senior clients, BCG consulting teams, and cross-functional technical stakeholders.
• Communicate with clarity, confidence, and executive presence across senior client stakeholders, consulting teams, and technical teams.
• Lead tough conversations on trade-offs, scope, delivery risk, platform choices, and implementation complexity with sound judgment and credibility.
• Bring a strong consulting mindset: structured problem solving, pragmatic decision-making, and the ability to navigate ambiguity without losing momentum.
• Lead and mentor engineers with high standards for architecture, code quality, documentation, DevOps, governance and production readiness.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Technology, Data Science or a related technical field.
• 8+ years of relevant experience in data engineering, data platforms, or enterprise AI/data solution delivery.
• Proven experience leading production-grade data engineering programs, with hands-on involvement in architecture, implementation, code review, deployment, and operationalization.
• Expert proficiency in at least one major programming language such as Python, Java, or Scala.
• Strong understanding of distributed computing and modern data platforms, including Spark-based processing, performance tuning, and large-scale data system design.
• Deep experience with technologies such as Spark, DBT, Databricks, Snowflake, Hive, Hadoop or equivalent big data and analytics ecosystems.
• Strong cloud engineering experience across AWS, Azure, or GCP, with expertise in at least one cloud ecosystem.
• On-ground exposure to GenAI implementation patterns, including RAG, retrieval pipelines, LLM application architecture, workflow design, evaluation, monitoring and deployment.
• Responsible use of AI tools across the SDLC to improve 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.
• Practical experience with data governance, metadata, cataloging, lineage, privacy and security in enterprise data environments.
• Strong client-facing communication skills, with the ability to influence senior stakeholders and handle ambiguity, trade-offs, and difficult delivery conversations.
• Prior consulting experience or experience working directly with senior stakeholders driving solution delivery.
• Exposure to ML engineering or advanced analytics integration in production environments.
• Good understanding of enterprise architecture transformations like Lakehouse and Data Mesh, along with its advantages and drawbacks.