Join Boston Consulting Group (BCG) X as a Lead Delivery AI Data Engineer and drive transformative data and AI initiatives. BCG X is a dynamic collective of over 3,000 tech experts committed to making a significant impact across industries.

We blend deep industry expertise with cutting-edge technology to deliver innovative solutions and foster growth for our clients. Our collaborative approach ensures we tackle complex challenges and create lasting value, pushing the boundaries of what's possible.

As a key member of our Delivery team, you will work alongside consulting experts, applying advanced data and AI methodologies. You'll provide critical subject matter expertise in data engineering, ensuring accelerated and high-quality execution for client projects.

You will architect, build, and manage production-grade data platforms and pipelines across diverse environments, including cloud, hybrid, and on-premise.

Your role involves leading end-to-end data engineering delivery, from ingestion and transformation to data modeling, orchestration, and productionization for analytics and AI use cases.

You'll design scalable, reliable data solutions for batch, streaming, real-time, and API-driven needs, ensuring they are maintainable and support critical business decisions.

Crafting trusted and reusable data foundations with robust practices in data quality, governance, cataloging, lineage, and access control is essential.

Lead GenAI implementations, demonstrating strong architectural judgment and hands-on experience with RAG, retrieval design, LLM application patterns, and production deployment.

Contribute to raising engineering standards through Python, Java, or Scala expertise, with a deep understanding of distributed systems and modern data platforms.

Develop reusable data engineering assets and patterns to accelerate delivery across client engagements.

Translate complex business challenges into scalable data and AI engineering solutions in collaboration with senior clients, consulting teams, and technical stakeholders.

A Bachelor's or Master's degree in Computer Science, Engineering, Technology, Data Science, or a related technical field is required.

We seek a minimum of 8 years of relevant experience in data engineering, data platforms, or enterprise AI/data solution delivery.

Proven leadership in production-grade data engineering programs, including hands-on experience in architecture, implementation, code review, deployment, and operationalization.

Expert proficiency in at least one major programming language like Python, Java, or Scala is essential.

Solid understanding of distributed computing and modern data platforms, including Spark-based processing, performance tuning, and large-scale data system design.

Extensive 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 deep expertise in at least one cloud ecosystem.

Demonstrated on-ground exposure to GenAI implementation patterns, including RAG, retrieval pipelines, LLM application architecture, workflow design, evaluation, monitoring, and deployment.

Apply responsible AI tool usage across the SDLC to enhance engineering productivity while upholding quality, security, and delivery accountability.

Practical experience building trusted data foundations, encompassing data modeling, pipeline productization, orchestration, CI/CD, containerization, and observability.

Experience with data governance, metadata, cataloging, lineage, privacy, and security in enterprise data environments.

Excellent client-facing communication skills, with the ability to influence senior stakeholders and manage ambiguity, trade-offs, and complex delivery discussions.

Preferred qualifications include prior consulting experience or direct engagement with senior stakeholders on solution delivery, and experience in data-rich industries like Retail or Telecom. Familiarity with NoSQL, streaming, messaging, event-driven architectures, ML engineering, or advanced analytics integration is beneficial, as are pragmatic build-versus-buy recommendations for enterprise data and GenAI platforms. A good understanding of enterprise architecture transformations like Lakehouse and Data Mesh is also advantageous.