Senior Data Engineer

We are seeking an experienced Full Stack Data Engineer with 5–6 years of industry experience. The ideal candidate will have a proven track record of working on live projects, preferably within the manufacturing or energy sectors. He/she will play a key role in developing, and maintaining scalable data solutions using Databricks and related technologies. Key Responsibilities: Develop, and deploy end-to-end data pipelines and solutions on Databricks, integrating with various data sources and systems. Collaborate with cross-functional teams to understand data, and deliver effective BI solutions. Implement data ingestion, transformation, and processing workflows using Spark (PySpark/Scala), SQL, and Databricks notebooks. Develop and maintain data models, ETL/ELT processes ensuring high performance, reliability, scalability and data quality. Build and maintain APIs and data services to support analytics, reporting, and application integration. Ensure data quality, integrity, and security across all stages of the data lifecycle. Monitor, troubleshoot, and optimize pipeline performance in a cloud-based environment. Write clean, modular, and well-documented Python/Scala/SQL/PySpark code. Integrate data from various sources, including APIs, relational and non-relational databases, IoT devices, and external data providers. Ensure adherence to data governance, security, and compliance policies. Required Skills and Experience: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. 5-6 years of hands-on experience in data engineering, with a strong focus on Databricks and Apache Spark. Strong programming skills in Python/PySpark and/or Scala, with a deep understanding of Apache Spark. Experience with Azure Databricks. Strong SQL skills for data manipulation, analysis, and performance tuning. Strong understanding of data structures and algorithms, with the ability to apply them to optimize code and implement efficient solutions. Strong understanding of data architecture, data modeling, ETL/ELT processes, and data warehousing concepts. Experience building and maintaining ETL/ELT pipelines in production environments. Familiarity with Delta Lake, Unity Catalog, or similar technologies. Experience working with structured and unstructured data, including JSON, Parquet, Avro, and time-series data. Familiarity with CI/CD pipelines and tools like Azure DevOps, version control (Git), and DevOps practices for data engineering. Excellent problem-solving skills, attention to detail, and ability to work independently or as part of a team. Strong communication skills to interact with technical and non-technical stakeholders. Requirements Experience with Delta Lake and Databricks Workflows. Exposure to real-time data processing and streaming technologies (Kafka, Spark Streaming). Exposure to data visualization tool Databricks Genie for data analysis and reporting. Knowledge of data governance, security, and compliance best practices.