Job Title:  Principal Product Architect | Data Engineering | Bengaluru | TTC - DOMESTIC

Job Summary

We are looking for an experienced Data Architect to design, build, and optimize modern Data Lakehouse platforms for large‑scale analytics and data processing. The role requires strong expertise in distributed data architectures, AWS cloud services, big data technologies, and data modeling, along with hands‑on leadership in defining scalable, secure, and high‑performance data solutions.

Key Responsibilities

• Design and own end‑to‑end Data Lakehouse architecture for batch and real‑time analytics
• Define data ingestion, transformation, storage, and consumption patterns using Spark‑based frameworks
• Architect scalable solutions leveraging AWS services and Databricks
• Establish standards for data modeling, partitioning, performance tuning, and cost optimization
• Lead architectural reviews and guide engineering teams on best practices
• Ensure data quality, governance, security, and reliability across the platform
• Collaborate with platform, DevOps, SRE, and security teams for production readiness
• Support major incidents, vulnerability management, and platform stability
• Drive CI/CD automation and Infrastructure‑as‑Code practices

Technical Skills & Experience

Programming & Scripting

• PySpark, Python, SQL
• MySQL, NoSQL
• Shell Scripting, Bash, YAML

Data & Database Technologies

• Relational Databases: PostgreSQL, Redshift
• Data Modeling: Star Schema
• Data Formats: Parquet, CSV, JSON
• Database Tools: DBeaver

Big Data & Lakehouse Technologies

• Apache Spark (architecture), Hadoop, MapReduce, YARN
• Hive, Sqoop, ETL frameworks
• Databricks (Data Engineering workloads)

Cloud & Platform

• AWS Cloud: S3, EC2, Glue, Redshift, Athena, EMR
• Monitoring & Ops: AWS CloudWatch, Dynatrace
• Networking & Infra: Amazon Route 53, F5 Load Balancer
• Infrastructure as Code: Terraform
• CI/CD & Deployment: Git, Azure DevOps

SRE, Security & Reliability

• Site Reliability Engineering concepts
• Major Incident Management
• Vulnerability and security best practices

Additional Exposure

• BigQuery, DataProc
• Kubernetes
• Windows & Linux environments