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 in
Understanding of narrow and wide transformations, actions, and lazy evaluations
How DataFrames are transformed, executed, and optimized in Spark
Use DataFrame API to explore, preprocess, join, and ingest data in Spark
Use Delta Lake to improve the quality and performance of data pipelines
Use SQL and Python to write production data pipelines to extract, transform, and load data into
tables and views in the Lakehouse
Understand the most common performance problems associated with data ingestion and how to
mitigate them
Monitor Spark UI: Jobs, Stages, Tasks, Storage, Environment, Executors, and Execution Plans
Configure a Spark cluster for maximum performance given specific job requirements
Configure Databricks to access Blob, ADL, SAS, user tokens, Secret Scopes and Azure Key Vault
Configure governance solutions through Unity Catalog and Delta Sharing
Use Delta Live Tables to manage an end-to-end pipeline with unit and integrations test
- Azure:
Intermediate/Advanced knowledge in
Azure Storage Account:
Provision Azure Blob Storage or Azure Data Lake instances
Build efficient file systems for storing data into folders with static or parametrized names, considering possible security rules and risks
Experience identifying use cases for open-source file formats like parquet, AVRO, ORC
Understanding optimized column-oriented file formats vs optimized row-oriented file formats
Implementing security configurations through Access Keys, SAS, AAD, RBAC, ACLs
Azure Data Factory:
Provision Azure Data Factory instances
Use Azure IR, Self-Hosted IR, Azure-SSIS to establish connections to distinct data sources
Use of Copy or Polybase activities for loading data
Build efficient and optimized ADF Pipelines using linked services, datasets, parameters, triggers, data movement activities, data transformation activities, control flow activities and mapping data flows
Build 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
