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Databricks Engineer

Job in 411001, Pune, Maharashtra, India
Listing for: Nice Software Solutions Pvt. Ltd.
Full Time position
Listed on 2026-02-22
Job specializations:
  • IT/Tech
    Data Engineer, Cloud Computing, Data Science Manager, Big Data
Job Description & How to Apply Below
Databricks Engineer

Experience - 5+ Years

Location - Pune/Nagpur (WFO)

We are seeking an experienced Data Engineer with strong hands-on expertise in Databricks on any cloud platform (AWS, Azure, or GCP), along with proven skills in SQL, Python, and PySpark. The ideal candidate will have a solid background in database design, data processing, and building scalable data pipelines in a cloud-based environment.

Key Responsibilities:

- Design, develop, and maintain scalable data pipelines and ETL processes using Databricks and Py Spark
- Work with structured and semi-structured data across various cloud platforms (AWS, Azure, or GCP)
- Write optimized and complex SQL queries for data extraction and transformation
- Implement data ingestion from various sources into the data lake/warehouse
- Collaborate with data analysts, business stakeholders, and other engineering teams to understand requirements and deliver solutions
- Monitor and improve the performance of existing data pipelines
- Ensure data quality, security, and governance across the data lifecycle

Required Skills &

Qualifications:

- 5–7 years of professional experience in Data Engineering or related roles
- Strong experience with Databricks on any cloud platform (AWS, Azure, or GCP)
- Proficiency in SQL, Python, and Py Spark
- Solid understanding of database structures, data modeling, and data warehousing concepts

- Experience with cloud data services and storage solutions
- Familiarity with CI/CD practices and version control tools like Git
- Excellent problem-solving and communication skills

Good to Have:

- Exposure to Delta Lake, Lakehouse architecture, or MLflow
- Knowledge of Apache Airflow or other workflow orchestration tools
- Experience in performance tuning and job optimization in Spark environments
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