Databricks Data Engineer 100% Remoto en Derevo

At Derevo we seek to empower companies and people to unlock the value of data in their organizations. We do this through the implementation of analytics processes and platforms with an approach that covers the complete cycle that they need to carry out to achieve it. Derevo started in 2010 with a simple idea, to create more than a company, a community and a space where everyone has the opportunity to build a dream. Do you want to know more about the vacancy?

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

 

-           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

Understanding and applying best practices for Source Control with Azure Repos Git integration