McKinsey & Company · 3 months ago
Data Engineer II - QuantumBlack, AI by McKinsey
Seattle, United States
Full-time
Onsite
Entry, Mid, Senior Level
$147K/yr - $150K/yr
2+ years expMcKinsey & Company is a global management consulting firm that focuses on driving lasting impact for clients. As a Data Engineer II, you will design, build, and optimize data platforms for advanced analytics and AI solutions, collaborating with clients and teams to manage secure data environments and unlock the value of complex datasets.
ConsultingManagement ConsultingProfessional Services
Responsibilities
Design, build, and optimize modern data platforms that power advanced analytics and AI solutions
Collaborate with clients and interdisciplinary teams to architect scalable pipelines, manage secure and compliant data environments, and unlock the value of complex datasets across industries
Develop a streaming data platform to integrate telemetry for predictive maintenance in aerospace systems
Implement secure data pipelines that reduce time-to-insight for a Fortune 500 utility company
Optimize large-scale batch and streaming workflows for a global financial services client, cutting infrastructure costs while improving performance
Develop pipelines for embeddings and vector databases to enable retrieval-augmented generation (RAG) for a global defense client
Work in cross-functional Agile teams with Data Scientists, Machine Learning Engineers, Designers, and domain experts to deliver high-quality analytics solutions
Partner closely with clients—from data owners to C-level executives—to shape data ecosystems that drive innovation and long-term resilience
Qualification
Required
Degree in Computer Science, Business Analytics, Engineering, Mathematics, or related field
2+ years of professional experience in data engineering, software engineering, or adjacent technical roles
Proficiency in Python, Scala, or Java for production-grade pipelines, with strong skills in SQL and PySpark
Hands-on experience with cloud platforms such as (AWS, GCP, Azure, Oracle) and modern data storage/warehouse solutions such as Snowflake, BigQuery, Redshift, and Delta Lake
Practical experience with Databricks, AWS Glue, and transformation frameworks like dbt, Dataform, or Databricks Asset Bundles
Knowledge of distributed systems such as (Spark, Dask, Flink) and streaming platforms (Kafka, Kinesis, Pulsar) for real-time and batch processing
Familiarity with workflow orchestration tools such as (Airflow, Dagster, Prefect), CI/CD for data workflows, and infrastructure-as-code (Terraform, CloudFormation)
Understanding of DataOps principles including pipeline monitoring, testing, and automation, with exposure to observability tools such as Datadog, Prometheus, and Great Expectations
Exposure to ML platforms such as (Databricks, SageMaker, Vertex AI), MLOps best practices, and GenAI toolkits (LangChain, LlamaIndex, Hugging Face)
Familiarity with vector databases and understanding of low latency serving patterns is a plus
Willingness to travel as required
Strong communication, time management, and resilience, with the ability to align technical solutions to business value
Benefits
Medical, mental health, dental and vision coverage
Telemedicine services
Life, accident and disability insurance
Parental leave and family planning benefits
Caregiving resources
A generous retirement contributions program
Financial guidance
Paid time off
Company
McKinsey & Company
McKinsey & Company is a global management consulting firm and trusted advisor by businesses, governments, and institutions.
10001+ employees
H1B Sponsorship
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2025 (612)
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Funding
Current Stage
Late StageLeadership Team
Guy Perry
Vice President
Abhyuadaya Shrivastava
Partner
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