Senior Cloud Data Engineer

We are looking for passionate data engineers who have an understanding of building, maintaining, and optimizing data pipelines in cloud environments (AWS | GCP | Azure). Our cloud business group works with top-tier silicon valley and European customers and helps them build production grade systems using cutting-edge cloud software technology.

At Xgrid, you will enjoy being part of an extremely talented, motivated team interacting with both business and development counterparts to capture key marketing requirements and translate them to products and services designed for our customers. The ideal Xgrid Cloud Data Engineer will have experience developing and delivering complex enterprise-grade mission-critical automated software systems for our cloud customers. 

Job Responsibilities

• Built scalable data pipelines in Python and Databricks, ingesting and integrating datasets from diverse structured and unstructured sources across cloud and on-prem environments.

• Designed and developed end-to-end ETL/ELT solutions, incorporating data modeling, data integration, and deriving data insights using Databricks Notebooks, Delta Lake, and PySpark.

• Designed, monitored, automated, and optimized development, test, and production infrastructure for big data workflows, leveraging Databricks Workflows, Python scripts, and CI/CD tools.

• Troubleshot and tuned performance of complex data ingestion, merging, and transformation pipelines across multiple architectures and tools including SQL, REST APIs, and cloud-native services.

• Deployed data solutions on AWS, Azure, and GCP, integrating cloud storage, compute, and analytics services with Databricks environments.

• Collaborated with senior engineers from Silicon Valley on architectural reviews, best practices, and building reusable components for analytics and ML-ready pipelines.

 Job Requirements

• 3–5 years of hands-on experience in Big Data engineering with a strong focus on the cloud.

• Proficient in Python, with hands-on experience using PySpark to build scalable, distributed data pipelines.

• Practical experience working with Databricks for data engineering, transformation, and analytics.

• Strong understanding of data engineering fundamentals: data warehousing, ETL/ELT, and handling structured and semi-structured data (e.g., JSON).

• Solid command of SQL for data manipulation, transformation, and performance tuning.

• Proven ability to design, build, and maintain production-grade data pipelines in cloud environments (Azure, AWS, or GCP).

• Familiar with CI/CD workflows, version control, and modern software engineering practices.

• Strong problem-solving, analytical thinking, and requirement gathering skills.

• Quick to learn new technologies and thrive in fast-paced environments.

• Excellent communication skills and a strong team-oriented mindset.

Preferred Skills

• Hands-on Python experience as a Software Engineer + Data Engineer building large-scale distributed data pipelines.

• Strong in SQL (Postgres, MySQL, RDS) and NoSQL (MongoDB, Elasticsearch, Cassandra, DynamoDB).

• Practical experience with Apache Airflow, Spark, Beam, and Databricks for data processing and orchestration.

• Cloud-native development on AWS, GCP, and Azure with end-to-end data solution delivery.

• Cloud certifications (AWS/GCP/Azure) demonstrating expertise in scalable, secure data architectures.