Key Responsibilities:
Architecture & Technical Leadership
- Define target-state data architecture using Databricks, Apache Spark, and AWS-native services
- Own and execute Snowflake divestiture with zero residual footprint and uninterrupted reporting
- Design scalable, secure, and cost-efficient batch and streaming data pipelines
- Establish architectural standards for data modeling, storage formats, and performance optimization
Data Engineering & Platform Strategy
- Design and implement ETL/ELT pipelines using Python, Spark, and SQL
- Build and optimize pipelines using AWS services such as S3, Lambda, EMR, and Databricks
- Enable real-time and near-real-time data processing using Kafka, Kinesis, and Spark Streaming
- Drive containerized deployments using Docker and Kubernetes
Orchestration, CI/CD & Infrastructure
- Define and lead workflow orchestration standards using Apache Airflow
- Implement and govern CI/CD pipelines using Git and Jenkins
- Own infrastructure provisioning using Terraform and/or CloudFormation
Data Governance & Enterprise Metrics
- Establish enterprise-wide data lineage, cataloging, and access control frameworks
- Define and manage metric dictionaries and KPI governance models
- Partner with analytics, product, and business teams to ensure metric alignment and trusted insights
Observability & Operational Excellence
- Implement monitoring, alerting, and observability across data platforms
- Define SLAs, SLOs, and operational playbooks for mission-critical analytics systems
- Mentor and guide engineers to elevate engineering standards across teams
