Data Engineer

Overview

Build and operate the data platform and analytics foundations behind Cisco's pricing, reporting and AI initiatives, from enterprise data warehouse through to governed, self-service analytics.

What you'll do

• Design, build, and operate batch and streaming data pipelines on Snowflake and/or Databricks
• Turn reference architectures and data contracts from the Principal AI Data Architect into robust, production-grade implementations
• Deliver reliable, observable, cloud-native ETL/ELT for both structured and unstructured data
• Modernize legacy jobs with measurable gains in latency, reliability, and cost
• Implement semantic models, dimensional schemas, and entity-centric structures that make data trustworthy and reusable
• Maintain the metadata, catalog, and lineage that let any downstream consumer trace an output back to its source
• Build low-latency APIs, materialized views, and serving layers that power dashboards, reporting, and ML/AI applications
• Embed data quality checks, RBAC/ABAC, masking, and encryption directly into pipelines
• Instrument workflows with logs and metrics so failures are caught before consumers see them
• Automate testing, deployment, and rollback through CI/CD

What you'll need

• Bachelor's degree in Computer Science or a related field
• 5–8 years of hands-on data engineering or software engineering experience building production data platforms
• Proven experience building and operating cloud-native pipelines on Databricks (PySpark, Delta Lake) and/or Snowflake, with modern orchestration tooling (Airflow, dbt, Dagster, or equivalent)
• Strong production experience with both batch and streaming data processing (Kafka, Spark Structured Streaming, Kinesis, or equivalent)
• Strong Python and SQL, with data modeling depth across dimensional, star/snowflake, and entity-centric patterns, and a track record of performance and cost tuning
• Practical experience implementing data quality checks, monitoring, and observability for production pipelines
• Hands-on with AWS and/or Azure services (S3, Glue, Lambda, ADLS, Data Factory, or equivalents)
• Hands-on with CI/CD (GitHub Actions, Jenkins, Azure DevOps) and containers (Docker/Kubernetes)
• Working knowledge of access control, masking, encryption, lineage, and auditability

Nice to have

• Practical exposure to Terraform or equivalent IaC tooling for provisioning and managing data infrastructure
• Experience with OpenSearch, pgvector, or similar search and retrieval platforms
• Familiarity with graph data stores such as Neo4j and entity-relationship modeling at scale
• Exposure to embedding pipelines, vector search, MLflow, or ML/LLMOps tooling, and general familiarity with how data pipelines feed model training and inference
• Interest in right-sizing cloud resources and improving the efficiency of automation and data pipelines

Details

• Location: Bangalore, India