Aws Data Engineer

Collaborate closely with stakeholders to comprehend data requirements, contributing to the design and development of robust Databricks-based solutions aligned with organizational objectives.

•       Engineer and document intricate data architectures, models, and infrastructure needs, ensuring scalability, security, and optimal performance within Databricks environments.

•       Provide expert guidance and leadership to cross-functional teams, including data engineers, analysts, and scientists, ensuring adherence to best practices throughout project cycles.

•       Demonstrate comprehensive expertise in Databricks, focusing on Spark-based data processing, ETL workflows, and relevant frameworks.

•       Design, implement, and manage complex data pipelines, ETL processes, and data integration strategies to facilitate efficient data flow within Databricks clusters.

•       Continuously monitor and fine-tune Databricks clusters, workloads, and queries to enhance performance, cost efficiency, and scalability.

•       Uphold stringent security standards and compliance requisites within Databricks solutions, implementing robust access controls and encryption measures.

•       Develop comprehensive technical documentation encompassing architecture diagrams, data flow representations, and procedural guides to support project teams and stakeholders.

•       Serve as the primary technical liaison for clients, ensuring transparent communication of project progress, technical decisions, and offering insights into best practices.

•       Collaborate closely with data engineers, scientists, and stakeholders to ensure seamless end-to-end datasolutions meeting project objectives.

•       Stay abreast of the latest trends and innovations in data engineering, Databricks, and related domains, recommending and implementing new tools and methodologies to enhance data processes.

Skills and Experience: 

•       High Proficiency in Databricks, specializing in data processing, and analytics leveraging Spark.

•       Strong grasp of data engineering concepts, encompassing data modelling, ETL processes, and datapipeline development.

•       Advanced proficiency in SQL for querying, transformation, and analysis of diverse datasets.

•       Expertise in Python programming for data manipulation, scripting, and automation within Databricks environments.

•       Strong familiarity with Apache Airflow for orchestrating and scheduling ETL workflows.

•       Knowledge of big data technologies such as Spark, Hadoop, and related frameworks.

•       Understanding of data security best practices and compliance standards.

•       Proficient in data modelling principles and data warehousing concepts.

•       Experience with version control systems like Git.

•       Exceptional communication and collaboration skills to effectively engage with cross-functional teams