Data Engineer

Data Engineer - QuantumBlack, AI by McKinsey Who You\'ll Work With Driving lasting impact and building long-term capabilities with our clients is not easy work. You are the kind of person who thrives in a high performance/high reward culture - doing hard things, picking yourself up when you stumble, and having the resilience to try another way forward. Your Impact You will work on real-world, high-impact projects across a variety of industries. You will have the opportunity to collaborate with QB/Labs teams and build complex and innovative ML systems to accelerate our work in AI and help solve business problems at speed and scale. You will experience the best environment to grow as a technologist and a leader. You will develop a sought-after perspective connecting technology and business value by working on real-life problems across a variety of industries and technical challenges to serve our clients on their changing needs. You will be surrounded by inspiring individuals as part of diverse and multidisciplinary teams. You will develop a holistic perspective of AI by partnering with the best design, technical, and business talent in the world as your team members. While we advocate for using the right tech for the right task, we often leverage the following technologies: Python, PySpark, the PyData stack, SQL, Airflow, Databricks, Kedro, Dask/RAPIDS, container technologies such as Docker and Kubernetes, cloud solutions such as AWS, GCP, and Azure, and more. Responsibilities Contribute to cross-functional problem-solving sessions with your team and our clients, from data owners and users to C-level executives, to address their needs and build impactful analytics solutions Have the opportunity to contribute to R&D projects and internal asset development Design and build GenAI applications (RAG, Agentic AI, etc) collaboratively with data scientists Map data fields to hypotheses and curate, wrangle, and prepare data for use in advanced analytics models Apply knowledge about clients data landscape and assess data quality Create and manage data environments and ensure information security standards are maintained at all times Design and build data pipelines for machine learning that are robust, modular, scalable, deployable, reproducible, and versioned Help to build and maintain the technical platform for advanced analytics engagements, spanning data science and data engineering work You will be part of our global Data Engineering community and you will work in cross-functional Agile project teams alongside Data Scientists, Machine Learning Engineers, other Data Engineers, Project Managers, and industry experts Your Qualifications and Skills Degree in computer science, engineering, mathematics, or equivalent experience 2+ years of relevant professional experience Ability to write clean, maintainable, scalable and robust code in an object-oriented language, e.g., Python, Scala, Java, in a professional setting Proven experience building data pipelines in production for advanced analytics use cases Experience working across structured, semi-structured and unstructured data Exposure to software engineering concepts and best practices, inc. DevOps, DataOps and MLOps would be considered a plus Familiarity with distributed computing frameworks, cloud platforms, containerization, and analytics libraries (e.g. pandas, numpy, matplotlib) Experienced on Big Data platforms and tools like AWS, Azure, GCP and tools like Spark, Kafka, Snowflake, GCS Data Proc, Azure DataFactory, AWS Glue, Apache Beam/Flink, Python/PySpark, etc. Commercial client-facing or senior stakeholder management experience would be beneficial Seniority level Associate Employment type Full-time Job function Analyst and Consulting Industries Business Consulting and Services #J-18808-Ljbffr