4 months ago
Gurgaon, IndiaMid Level / Senior
Responsibilities
- Own the design, build, testing, deployment, and operation of data engineering modules, pipelines, and data products across cloud, hybrid, and on-premise environments.
- Deliver end-to-end data engineering components for ingestion, transformation, data modeling, orchestration, serving, and productionization.
- Build reliable batch, streaming, and API-driven data solutions for client-facing analytics and AI delivery.
- Create reusable data assets using data quality, governance, cataloging, lineage, observability, and access control practices.
- Implement GenAI and agentic AI workflows involving retrieval, tool-calling, orchestration, evaluation, monitoring, and production deployment.
- Write and review production-level SQL and code in Python, Java, or Scala for distributed data systems and cloud data platforms.
- Collaborate with clients, consulting teams, data scientists, architects, and engineers to translate requirements into scalable data and AI solutions.
- Explain technical trade-offs and independently own assigned modules or workstreams while mentoring junior engineers.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, Technology, Data Science, or a related technical field.
- 4–6 years of relevant experience in data engineering, data platforms, analytics engineering, or enterprise AI/data solution delivery.
- Advanced SQL proficiency, including complex querying, performance optimization, data modeling, and large analytical datasets.
- Strong proficiency in at least one of Python, Java, or Scala.
- Experience with distributed computing, Spark-based processing, orchestration, performance tuning, and scalable data system design.
- Hands-on experience with GenAI and agentic AI implementation patterns, including tool-calling, agent orchestration, retrieval integration, workflow automation, evaluation, and operational monitoring.
- Experience with Spark, dbt, Databricks, Snowflake, Hive, Hadoop, or equivalent ecosystems.
- Hands-on experience with AWS, Azure, or GCP and working depth in at least one cloud ecosystem.
- Experience with orchestration, CI/CD, containerization, observability, and DevOps practices using tools such as Airflow, GitHub Actions, Docker, or Kubernetes.
- Experience implementing data modeling, data quality, metadata, cataloging, lineage, privacy, security, and access control practices.
- Prior consulting or stakeholder-facing solution delivery experience is preferred.
- Experience in retail, telecom, or other data-rich industries is preferred.
- Exposure to ML engineering, feature pipelines, MLflow, model serving, analytics platform enablement, lakehouse, medallion architecture, data mesh, enterprise data governance, MCP, or A2A is preferred.
Tech Stack
Apache AirflowApache HadoopApache HiveApache SparkAWSAzureDatabricksdbtDockerGitHub ActionsGoogle Cloud PlatformJavaKubernetesMLflowPythonScalaSnowflakeSQL
Categories
AI ApplicationsData Engineering
About BCG
Boston Consulting Group provides management consulting, technology, and design services to large enterprises, governments, and nonprofits, helping with strategy, digital transformation, and operations. The firm works on a project-based, fee-for-service model and also incubates products and ventures through BCG X. Founded in 1963 and headquartered in Boston, it is a global partnership with offices worldwide.