Data Engineer (Architect)

Job Description

Architect, administer, and optimize Databricks workspaces, clusters, jobs, and workflows.

Design and develop scalable Azure-based data engineering solutions, including Data Lake, Data Factory, Synapse, Key Vault, etc.

Build high-performance data pipelines using Python and PySpark.

Work with Hadoop-based platforms and distributed storage systems.

Implement and manage real-time data streaming solutions and ingestion pipelines.

Ensure proper configuration, monitoring, governance, and performance tuning of data platforms.

Integrate CI/CD pipelines on Azure and work with containerization technologies (Docker/K8s).

Develop and optimize SQL queries, stored procedures, and data models using MS SQL.

Collaborate with architects, data scientists, and engineering teams to deliver enterprise-grade solutions.

Define best practices, coding standards, and data engineering frameworks across the organization.

Troubleshoot complex data issues and ensure high availability of mission-critical data systems.

Requirements

Minimum 10+ years of total IT experience.

Minimum 6+ years strong experience in the following areas: Databricks Administration , Azure Cloud , Python & PySpark , Hadoop ecosystem , Data streaming & data pipeline engineering , MS SQL

Proven experience architecting large-scale distributed data systems.

Strong understanding of data lakes, data warehousing, and big data architecture patterns.

Nice-to-Have Skills

Experience with Apache Spark optimization and tuning.

Hands-on experience with CI/CD pipelines on Azure (GitHub Actions, Azure DevOps, etc.).

Knowledge of containers (Docker, Kubernetes).

Understanding of MLOps or advanced analytics integrations.

Originally posted on Himalayas