Databricks Engineer

Actively Reviewing the Applications

McLaren Strategic Solutions (MSS)

India, Karnataka Full-Time On-site INR 1–4 LPA

Posted 3 weeks ago • Apply by June 15, 2026

Job Description

About Us

Next Generation of Technology Consulting

Our approach is built on delivering value by combining our powerful ecosystem of platforms with capital efficient execution.

We bring together deep domain expertise and our strength in technology to help the world’s leading businesses build their digital core, optimize operations, accelerate revenue growth and deliver tangible outcomes at speed and scale.

Job Description

Key Responsibilities

Design and develop scalable data ingestion pipelines using Databricks and Apache Spark.
Build and maintain ETL/ELT workflows to ingest structured and semi structured data from various source systems.
Implement data transformation logic using PySpark, SQL, and Delta Lake.
Integrate data from APIs, databases, file systems, and streaming platforms.
Optimize data processing performance and manage large-scale data workloads.
Collaborate with Data Architects, Business Analysts, and QA teams to ensure accurate data delivery.
Implement monitoring, logging, and error handling mechanisms for ingestion pipelines.
Support CI/CD deployment processes for data engineering solutions.
Ensure adherence to data governance, security, and data quality standards.

Requirements

Required Qualifications

4+ years of experience in Data Engineering or Big Data development.
Strong experience with Databricks and Apache Spark.
Proficiency in Python (PySpark) and SQL.
Experience building data pipelines in cloud environments such as AWS, Azure, or GCP.
Knowledge of data ingestion frameworks and ETL tools.
Experience working with structured and semi-structured data formats (JSON, Parquet, CSV, Avro).
Familiarity with version control tools such as Git.

Preferred Qualifications

Experience with Delta Lake architecture.
Knowledge of workflow orchestration tools such as Airflow or Azure Data Factory.
Experience with streaming platforms such as Kafka or Event Hub.
Exposure to CI/CD pipelines and DevOps practices for data platforms.