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QuantumBlack, AI by McKinsey · 1 hour ago

Data Engineer II - QuantumBlack, AI by McKinsey (Critical Industries)

New York, NY

Full-time

Hybrid

Mid, Senior Level

2+ years exp

QuantumBlack, AI by McKinsey is focused on driving lasting impact and building long-term capabilities for clients. As a Data Engineer II, you will design, build, and optimize data platforms that power advanced analytics and AI solutions while collaborating with interdisciplinary teams to manage secure data environments and unlock the value of complex datasets.

AnalyticsArtificial Intelligence (AI)ConsultingData VisualizationInformation TechnologySoftwareSoftware Engineering

Growth Opportunities

No H1B

U.S. Citizen Only

Responsibilities

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

Qualification

Data EngineeringPythonCloud PlatformsSQLPySparkDatabricksDistributed SystemsDataOps PrinciplesWorkflow OrchestrationResilienceCommunicationTime Management

Required

U.S. Citizenship is required for this role (you must be able to be staffed on Critical Industries work which includes Government, Defense, Aerospace, Utilities, etc.)

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)

Willingness to travel as required

Strong communication, time management, and resilience, with the ability to align technical solutions to business value