DATABRICKS Data Engineer

Job description

**Greetings from TCS Recruitment Team!** \\*Face to Face\\* For all those **DATABRICKS Data Engineer** we are coming bigger with the plan of **Face to Face Drive on 11th July,2026 (Saturday) in Vishakhapatnam.** It is a Face to Face interview planned to attract great Talents in **DATABRICKS Data Engineer** We believe that your skills and expertise are a better match for the skills we are looking for. Skill: **DATABRICKS Data Engineer** **(Face to Face)** Years of experience: **5 to 12 Years** Location **: Vishakhapatnam** Date: **11th July,2026 (Saturday) (Face to Face)** Drive Time: **9AM to 2 PM** **Total IT Experience (in Yrs.)** 5-12yrs **Relevant Experience Required (in Yrs.)** - Hands-on experience in **developing, maintaining, and optimizing data pipelines** using **Databricks** . - Knowledge of **ETL/ELT pipelines, data ingestion, transformation, and orchestration** . - Exposure to **data lake, data warehouse, or lakehouse architectures** . - Experience with **data migration, integration, and automation** . - Collaboration with senior engineers, architects, and analytics teams for solution delivery. - Familiarity with **security, access control, and compliance** best practices. - Working experience in **Agile and DevOps environments** . **Language Requirement** : **English** **Key words to search in resume** Databricks Data Engineer, Spark, PySpark, Delta Lake, ETL, ELT, Data Pipeline, Python, SQL, Data Lake, Lakehouse, Data Warehouse, AWS, Azure, GCP, Airflow, CI/CD, Data Integration, Stream Processing, Batch Processing **Technical/Functional Skills -MUST HAVE SKILLS** - Hands-on experience with **Databricks platform** , including: - **PySpark / Spark SQL** for data processing and transformation - **Delta Lake** for ACID-compliant data storage - **Notebooks** for workflow orchestration and collaborative development - Strong programming skills in **Python** and **SQL** . - Experience in **data ingestion** from batch and streaming sources. - Knowledge of **ETL/ELT design patterns** and data pipeline optimization. - Familiarity with **cloud data storage and compute environments** (AWS, Azure, or GCP). - Basic understanding of **workflow orchestration tools** (Airflow, Databricks Jobs). - Exposure to **DevOps concepts** , CI/CD pipelines, and version control (Git). - Awareness of **data security** , access control, and compliance considerations. - Understanding of **performance tuning** , partitioning, and caching strategies in Spark. **Secondary Skills** - Knowledge of **BI/analytics tools** such as Power BI, Tableau, or Looker. - Familiarity with **containerization** (Docker, Kubernetes) for workload deployment. - Basic knowledge of **machine learning pipelines** in Databricks (MLflow, Spark MLlib). - Understanding of **Agile methodologies** and collaborative development practices. - Exposure to **multi-cloud data integration** is a plus. **Responsibilities** - Develop and maintain **scalable data pipelines** using Databricks (PySpark, Delta Lake). - Ingest, process, and transform structured and unstructured data from multiple sources. - Collaborate with senior engineers and architects to implement **data lakehouse or warehouse solutions** . - Optimize pipelines for **performance, reliability, and cost efficiency** . - Implement **ETL/ELT workflows** for batch and streaming data processing. - Ensure **data quality, validation, and error handling** in pipelines. - Support **DevOps integration** , CI/CD pipelines, and automated deployments. - Document data processes, workflows, and pipeline configurations. - Troubleshoot and resolve pipeline or processing issues. - Work closely with analytics, BI, and data science teams to deliver **actionable insights** . - Learn and apply **new Databricks features** and emerging data engineering best practices.