Data Engineer II - QuantumBlack, AI by McKinsey
- Continuous learning: Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast-paced learning experience, owning your journey.
- A voice that matters: From day one, we value your ideas and contributions. You’ll make a tangible impact by offering innovative ideas and practical solutions, all while upholding our unwavering commitment to ethics and integrity. We not only encourage diverse perspectives, but they are critical in driving us toward the best possible outcomes.
- Global community: With colleagues across 65+ countries and over 100 different nationalities, our firm’s diversity fuels creativity and helps us come up with the best solutions for our clients. Plus, you’ll have the opportunity to learn from exceptional colleagues with diverse backgrounds and experiences.
- World-class benefits: On top of a competitive salary (based on your location, experience, and skills), we provide a comprehensive benefits package to enable holistic well-being for you and your family.
- Degree in Computer Science, Engineering, Mathematics, or equivalent experience
- 2–5+ years of professional experience in building and deploying data solutions
- Strong coding skills in Python, Scala, or Java, with the ability to write clean, maintainable, and scalable code
- Proven experience in building and maintaining production-grade data pipelines for advanced analytics use cases
- Experience working with structured, semi-structured, and unstructured data
- Hands-on expertise in containerization and orchestration using Docker and Kubernetes for scalable production systems
- Familiarity with distributed computing frameworks (e.g., Spark, Dask), cloud platforms (e.g., AWS, Azure, GCP), and analytics libraries (e.g., pandas, numpy, matplotlib)
- Exposure to DevOps, DataOps, and MLOps concepts and best practices
- Experience with core technologies such as Python, PySpark, SQL, Airflow, Databricks, Kedro, Dask/RAPIDS, Docker, Kubernetes, and cloud services (AWS/GCP/Azure)
- Experience with Generative AI (GenAI) or agentic systems is a strong plus
- Prior client-facing or senior stakeholder management experience is beneficial
- Excellent time management and communication skills (verbal and written), with flexibility to adapt across audiences; willingness to travel as needed
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