Data Solutions Architect

Neuroniks Inc.; India; Presencial; INR 1.600.000 - 2.300.000; Jornada completa

Descripción de la vacante

Keka Technologies Private Limited is seeking a data platform engineer to design enterprise data platforms for health plans, PACE organizations, and pharma clients. You will own the full data stack—from lakehouse architecture to streaming pipelines handling millions of daily claims.

You will work with AWS data services, Spark, Kafka, and modern tools like dbt, Airflow, and Snowflake or Databricks to build scalable, compliant analytics solutions.

Formación

• Experience designing large-scale data platforms, preferably in healthcare.
• Experience with cloud data services and modern data stacks.
• Strong knowledge of data modeling and streaming architectures.

Responsabilidades

• Architect data lakes, warehouses, and lakehouse platforms on AWS.
• Design scalable ETL/ELT pipelines for high-volume healthcare data.
• Implement dimensional modeling (star/snowflake) for analytics.
• Build real-time streaming pipelines using Kafka, Flink, and Kinesis.
• Define data governance including lineage, masking, catalogs, and access policies.
• Collaborate with AI/ML teams on feature stores and training data pipelines.

Conocimientos

AWS data services

PySpark

Kafka

Airflow

Snowflake

Databricks

dbt

Flink

Descripción del empleo

Design modern, HIPAA-compliant enterprise data platforms powering analytics, AI/ML workloads, and real-time intelligence for health plans, PACE organizations, and pharma clients. You'll own the full data stack — from lakehouse architecture to streaming pipelines handling millions of daily pharmacy claims.

KEY RESPONSIBILITIES

• Architect data lakes, warehouses, and lakehouse platforms on AWS
• Design scalable ETL/ELT pipelines for 800K+ daily claim records
• Implement dimensional modeling (star/snowflake) for healthcare analytics
• Build real-time streaming pipelines: Kafka (MSK), Flink, and Kinesis
• Define data governance: lineage, PII masking, catalog, and access policies
• Collaborate with AI/ML teams on feature stores and training data pipelines

WHAT WE'RE LOOKING FOR

• Deep AWS data services: S3, Glue, Redshift, Athena, Lake Formation
• Apache Spark (PySpark), Kafka, Flink streaming architectures
• Modern data stack: dbt, Airflow, Snowflake or Databricks

CORE TECHNOLOGIES

• Terraform · Apache Iceberg · Delta Lake