We are looking for a Senior Data Engineer to design, build, and optimize large-scale data processing systems supporting healthcare analytics and operational reporting. This role will involve working closely with DataOps, DevOps, and QA teams to enable scalable and reliable data pipelines.

Key Responsibilities

Design and implement ETL/ELT pipelines using Python and PySpark

Develop scalable data workflows using Apache Spark and AWS Glue

Collaborate with QA and DevOps to integrate CI/CD and testing automation

Manage data lake structures and ensure data quality, lineage, and auditability

Optimize and monitor performance of batch and streaming pipelines

Build infrastructure as code (IaC) using tools like Terraform, GitHub Actions

Work across structured, semi-structured, and unstructured healthcare datasets

Required Technical Skills

Core & Deep Knowledge Assessment:

Python

PySpark

SQL (including Window functions and CASE)

AWS Glue, S3, Lambda

Apache Spark

Apache Airflow

Delta Lake/Data Lakehouse Architecture

CI/CD (Terraform, GitHub Actions)

ETL/ELT pipeline design and optimization

Basic Overall Knowledge Assessment:

Kafka

Data modeling and normalization

Unix/Linux

Infrastructure as Code (IaC)

Cloud storage, IAM, and networking fundamentals (AWS)

Git version control

Healthcare data domain knowledge