Practo - Data Platform Engineer

About The Role

We are looking for a highly hands-on Data Platform Engineer to join our AI Labs team in Bengaluru. This role will be responsible for architecting, building, and scaling our end-to-end data platform, bridging traditional data engineering with modern AI infrastructure.

You will own the design and implementation of data pipelines, lakehouse architecture, multi-region data systems, and AI/ML-powered data processing workflows. From Change Data Capture (CDC) and ETL/ELT pipelines to LLM inference and multimodal data redaction, you will build reliable, scalable, and cost-efficient data infrastructure.

Roles & Responsibilities

• End-to-End Data Architecture & Engineering :
• Design, build, and maintain end-to-end data pipelines, covering CDC ingestion from production databases to production-grade lakehouse storage.
• Develop and scale workflow orchestration using Temporal and data transformation pipelines using dbt.
• Build standardized dimensional data models and optimized relational/materialized views to support analytics and business intelligence.
• Modernize legacy data workflows by replacing traditional DAG runners and cron-based batch jobs with scalable orchestration frameworks.
• Multi-Region Architecture & Data Residency :
• Design and implement multi-region data architectures across cloud environments, ensuring compliance with data residency and sovereignty requirements.
• Establish secure data isolation mechanisms to ensure sensitive patient and transaction data remains within permitted geographical boundaries.
• Build compliant cross-region replication, synchronization, failover, and data-routing mechanisms.
• AI/ML Integration & Multimodal Data Processing :
• Integrate machine learning models into data pipelines to identify and redact sensitive PII/PHI across text, images, audio, and video.
• Build and operationalize embedding generation, LLM inference, and semantic search pipelines.
• Develop scalable workflows for AI-powered data processing and multimodal content analysis.
• Lakehouse Infrastructure & Platform Enablement :
• Design and optimize open lakehouse architectures using Apache Iceberg.
• Implement efficient partitioning, ACID transactions, and time-travel capabilities for large-scale datasets.
• Provision and manage secure, scalable Jupyter Notebook environments for data science and exploratory analytics.
• Cloud Infrastructure, DevOps & FinOps :
• Design, deploy, and manage AWS infrastructure using Infrastructure as Code (Terraform or AWS CDK).
• Establish cost-monitoring frameworks to track cost-per-outcome across data and AI pipelines.
• Own production reliability through monitoring, alerting, data quality checks, and pipeline freshness tracking.
  
  

Required Skills & Technical Expertise

• Strong experience in data engineering, data platform development, and large-scale distributed data systems.
• Proficiency in SQL and Python for data engineering and pipeline development.
• Hands-on experience building CDC-based ingestion and ETL/ELT pipelines.
• Experience with workflow orchestration frameworks such as Temporal.
• Working knowledge of dbt for data transformation and modeling.
• Experience with Apache Iceberg or other modern lakehouse technologies.
• Experience with AWS cloud infrastructure and Infrastructure as Code tools such as Terraform or AWS CDK.
• Exposure to ML/LLM inference pipelines, embedding generation, and AI-powered data processing.
  
  

Hiring Note

We are hiring for two complementary SE II positions from the same candidate pool. Both positions are individual contributor roles focused on deep technical architecture and execution, with no formal people-management or team-mentoring responsibilities.

(ref:hirist.tech)