Sr. Data Engineer – Healthcare

Overview

At Anblicks, we help enterprises modernize their data ecosystems through Data Engineering, Cloud, and AI-driven solutions. We partner with global clients to build scalable data platforms, advanced analytics infrastructure, and AI-enabled products that drive measurable business outcomes.

We are seeking a highly skilled Sr. Data Engineer – Healthcare to build and scale next-generation cloud data platforms that support machine learning, analytics, and enterprise reporting use cases. This role will work closely with Data Scientists, ML Architects, and Product Teams to design and implement scalable data lake and enterprise data warehouse (EDW) solutions from the ground up.

Role Overview

As a Sr. Data Engineer at Anblicks, you will:

• • Design and build scalable cloud-native data platforms.
  • Develop robust data ingestion, ETL, and data warehousing solutions.
  • Enable ML infrastructure and model development pipelines.
  • Drive adoption of modern cloud database and big data technologies.
  • Ensure governance, security, and performance optimization across data ecosystems.
    

• • Partner with ML Architects and Data Scientists to design scalable model development and model management infrastructure in the cloud.
  • Build and optimize next-generation data lakes and EDW solutions.
  • Champion migration to modern cloud-based data stacks.
  • Contribute to architecture and design decisions across the enterprise data landscape.
    

• • Design and develop scalable data ingestion, transformation, and processing pipelines.
  • Build robust ETL/ELT workflows to support healthcare analytics and ML use cases.
  • Develop high-performance data pipelines for structured and semi-structured datasets.
  • Support ingestion and management of healthcare data sources including claims, clinical, provider, payer, and third-party datasets.
  • Ensure seamless integration across distributed systems.
    

• • Work extensively with distributed data processing frameworks (Spark, PySpark, Hadoop ecosystem).
  • Build solutions using modern cloud databases such as Snowflake, AWS Redshift, Azure SQL, or similar platforms.
  • Develop and manage streaming and near real-time data pipelines using Kafka or Spark Streaming.
  • Implement role-based access control, security policies, and compliance measures.
  • Contribute to CI/CD processes, environment management, and infrastructure optimization.
    

• • Implement unit testing, data validation, and quality checks.
  • Troubleshoot and resolve issues across QA, pre-production, and production environments.
  • Maintain comprehensive documentation of architecture, services, and workflows.
  • Support performance tuning and optimization of data pipelines and databases.
  • Ensure compliance with healthcare data regulations (e.g., HIPAA).
    

• • 7–10 years of experience in Data Engineering or Big Data development.
  • Strong background in ETL/ELT development, data warehousing, and data pipelining.
  • 5+ years of hands-on expertise with Snowflake and cloud-native data platforms.
  • 5+ years of experience working with distributed data processing frameworks (Spark, PySpark, Hadoop ecosystem).
  • 5+ years of experience with cloud databases (AWS, Azure, GCP).
  • Strong programming skills in Python, PySpark, Scala, or Java.
  • Experience building scalable data ingestion and processing pipelines.
  • Experience with container-based deployments (Docker, Kubernetes, EKS/ECS or similar).
  • Understanding of data lake architectures and modern cloud migration strategies.
  • Strong understanding of software design and architecture principles.
  • Experience implementing data quality checks and testing frameworks.
  • Bachelor’s degree in Computer Science, Engineering, or related discipline.
    

• • Snowflake and/or Databricks certifications.
  • Experience with cloud ETL tools such as AWS Glue or similar.
  • Experience building real-time and streaming data pipelines.
  • Exposure to MLOps tools such as MLflow or Kubeflow.
  • Experience working with end-to-end ML platforms (AWS SageMaker, Azure ML, Databricks, etc.).
  • Experience managing and integrating third-party healthcare datasets.
  • Experience working in healthcare analytics environments (payer, provider, life sciences).
    

• • Work on cutting-edge cloud and data engineering programs.
  • Build scalable ML-ready data platforms for global enterprises.
  • Collaborate with high-performing Data & AI teams.
  • Exposure to modern cloud-native architectures and big data ecosystems.
  • Be part of a rapidly growing data transformation organization.