Data Engineer Senior

We are looking for your talent ✋

*** Data Engineer Senior**

**‍**

**El perfil deseado debe tener al menos 5 años de experiência práctica en el diseño, establecimiento y mantenimiento de sistemas de gestión y almacenamiento de datos. Hábil en la recopilación, procesamiento, limpieza y despliegue de grandes conjuntos de datos, la comprensión de los modelos de datos ER, y la integración con múltiples fuentes de datos. Eficaz en el análisis, la comunicación y la propuesta de diferentes formas de crear almacenes de datos, lagos de datos, conductos de extremo a extremo y soluciones de Big Data para los clientes, ya sea en estrategias por lotes o en streaming.**

Será muy importante que tengas los siguientes conocimientos/experiência:

- ** Inglés B2+ o más** (llevarás proyectos 100% con el idioma, por lo que será indispensable el dominio hablado y escrito)

**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
- **Apache Kafka, Azure Event Hubs or AWS Kinesis**

Intermediate/Advanced knowledge in

Architecture and fundamental concepts of event streaming platforms, including producers, consumers, topics, partitions, and consumer groups

Configuration, deployment, and management of event streaming clusters/services for high availability, scalability, and fault tolerance

Performance tuning and optimization of event streaming clusters, including message retention, partition sizing, and data replication

Implementing common usage patterns such as asynchronous messaging, real-time stream processing, and end-to-end data pipelines for real-time data ingestion and processing

Security best practices for event streaming platforms, including encryption, authentication, and access control mechanisms

**Además, valoramos mucho es que a nível personal encajes con la cultura de Derevo**:

- Capacidad de adaptación y superación. Buscamos personas que se quieran comer el mundo, proactivas y flexibles, a las que no les importe adaptarse a los cambios tecnológicos y metodologías existentes.
- Capacidad analítica y capaz de transmitir confianza en entornos de incertidumbre: debes tener capacidad para gestionar los problemas y verlos como punto de partida para la mejora. Tener y generar