Databricks Data Engineer

About Us:At Derevo, we are dedicated to empowering businesses and individuals to unleash the value of data within organizations. We achieve this by implementing analytics processes and platforms with a comprehensive approach covering the entire cycle necessary to achieve it.Derevo started in 2010 with a simple idea - to create more than a company, but a community and a space where everyone has the opportunity to build a dream.At Derevo, we believe in human talent that is free and creative. Being human is our superpower**Databricks Data Engineer****Summary**:The desired profile should have at least 5 years hands-on experience in designing, establishing, and maintaining data management and storing systems. Skilled in collecting, processing, cleaning, and deploying large datasets, understanding ER data models, and integrating with multiple data sources. Efficient in analyzing, communicating, and proposing different ways of building Data Warehouses, Data Lakes, End-to-End Pipelines, and Big Data solutions to clients, either in batch or streaming strategies.**Technical Proficiencies**:- SQL:Data Definition Language, Data Manipulation Language, Intermediate/advanced queries for analytical purpose, Subqueries, CTEs, Data types, Joins with business rules applied, Grouping and Aggregates for business metrics, Indexing and optimizing queries for efficient ETL process, Stored Procedures for transforming and preparing data, SSMS, DBeaver- Python:Experience in object-oriented programming, Management and processing datasets, Use of variables, lists, dictionaries and tuples, Conditional and iterating functions, Optimization of memory consumption, Structures and data types, Data ingestion through various structured and semi-structured data sources, Knowledge of libraries such as pandas, numpy, sqlalchemy, Must have good practices when writing code- Databricks / Pyspark:Intermediate knowledge inUnderstanding of narrow and wide transformations, actions, and lazy evaluationsHow DataFrames are transformed, executed, and optimized in SparkUse DataFrame API to explore, preprocess, join, and ingest data in SparkUse Delta Lake to improve the quality and performance of data pipelinesUse SQL and Python to write production data pipelines to extract, transform, and load data intotables and views in the LakehouseUnderstand the most common performance problems associated with data ingestion and how tomitigate themMonitor Spark UI: Jobs, Stages, Tasks, Storage, Environment, Executors, and Execution PlansConfigure a Spark cluster for maximum performance given specific job requirementsConfigure Databricks to access Blob, ADL, SAS, user tokens, Secret Scopes and Azure Key VaultConfigure governance solutions through Unity Catalog and Delta SharingUse Delta Live Tables to manage an end-to-end pipeline with unit and integrations test- Azure:Intermediate/Advanced knowledge inAzure Storage Account:Provision Azure Blob Storage or Azure Data Lake instancesBuild efficient file systems for storing data into folders with static or parametrized names, considering possible security rules and risksExperience identifying use cases for open-source file formats like parquet, AVRO, ORCUnderstanding optimized column-oriented file formats vs optimized row-oriented file formatsImplementing security configurations through Access Keys, SAS, AAD, RBAC, ACLsAzure Data Factory:Provision Azure Data Factory instancesUse Azure IR, Self-Hosted IR, Azure-SSIS to establish connections to distinct data sourcesUse of Copy or Polybase activities for loading dataBuild efficient and optimized ADF Pipelines using linked services, datasets, parameters, triggers, data movement activities, data transformation activities, control flow activities and mapping data flowsBuild Incremental and Re-Processing Loads- CICD (deseable)**Process Automation**: Automate the deployment, scaling, and de-scaling of Azure Databricks clusters using tools like ARM Templates, Terraform, or Azure DevOps Pipelines.**Monitoring and Performance Optimization**: Set up alerts and monitor key performance metrics in Azure Databricks using Azure Monitor and other monitoring tools. Optimize cluster and workload performance to ensure efficiency and scalability.**Security and Compliance**: Implement security controls and compliance policies in Azure Databricks**Integration with Azure Services**: Integrate Azure Databricks with other Azure services such as Azure Data Lake Storage, Azure SQL Database, Azure Synapse Analytics, and Azure DevOps to create end-to-end data analytics solutions.**Configuration and Secrets Management**: Manage configurations and sensitive secrets using Azure Key Vault or other secrets management solutions. Ensure the security of credentials and access keys.**Training and Support**: Provide training and technical support to development and data analytics teams in the effective use of Azure Databricks. Documen