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, testing, deployment, and operation of data engineering modules, pipelines and data products across cloud, hybrid and on-premise environments.
• Delivering end-to-end data engineering components across ingestion, transformation, data modeling, orchestration, serving and productionization for analytics and AI use cases.
• Building reliable batch, streaming and API-driven data solutions that are maintainable, well-documented and ready for client-facing delivery.
• Creating trusted and reusable data assets with sound practices across data quality, governance, cataloging, lineage, observability and access control.
• Implementing GenAI and Agentic AI workflows that include retrieval, tool-calling, orchestration, evaluation, monitoring and production deployment for enterprise use cases.
• Writing and reviewing high-quality SQL and production-level code in Python, Java or Scala, with hands-on depth in distributed data systems and cloud data platforms.
• Using AI tools responsibly across the SDLC to improve engineering productivity while maintaining code quality, security, review discipline and delivery accountability.
• Working directly with clients, BCG consulting teams, data scientists, technical architects, and engineers to translate requirements into scalable data, GenAI, and Agentic AI solutions.
• Communicate clearly and confidently with clients, consulting teams, and technical stakeholders.
• Explain engineering choices, trade-offs, risks, dependencies, and implementation constraints in a concise and business-relevant manner.
• Bring structured problem solving, analytical thinking, and pragmatic judgment to ambiguous client and delivery situations.
• Independently own assigned modules or workstreams while escalating risks, dependencies, and architectural decisions at the right time.
• Mentor junior engineers and contribute to strong standards for code quality, documentation, testing, DevOps, governance, and production readiness.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Technology, Data Science, or a related technical field.
• 4–6 years of relevant experience in data engineering, data platforms, analytics engineering, or enterprise AI/data solution delivery.
• Advanced SQL proficiency, including complex querying, performance optimization, data modeling, and working with large analytical datasets.
• Strong proficiency in at least one major programming language such as Python, Java, or Scala.
• Solid understanding of distributed computing and data platform engineering, including Spark-based processing, orchestration, performance tuning, and scalable data system design.
• Hands-on exposure to GenAI & Agentic AI implementation patterns, including tool-calling, agent orchestration, retrieval integration, workflow automation, evaluation, and operational monitoring
• Hands-on experience with cloud data services across AWS, Azure, or GCP, with working depth in at least one cloud ecosystem.
• Practical experience implementing data modeling, data quality, metadata, cataloging, lineage, privacy, security, and access control practices within assigned delivery scope.
• Prior consulting experience or experience working directly with stakeholders in solution delivery environments.
• Exposure to ML engineering or advanced analytics integration in production environments, including feature pipelines, MLflow, model serving, or analytics platform enablement.
• Familiarity with lakehouse, medallion architecture, data mesh, and enterprise data governance concepts.
• Familiarity with emerging agent interoperability patterns such as MCP, A2A, or equivalent enterprise agent communication frameworks.