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Data EngineerOutdefine Partner

Web3

10-50

San Francisco, CA, USA

About the job

Overview:

Job Overview
Location: Hybrid, Guadalajara - MX
Long term Contract: All MX benefits
Mandatory: 5+ years
Experience with: Azure, Spark/Databricks, Snowflake, ETLs
Hiring Process: 3 rounds
Roles and Responsibilities:
 Designs, develops, optimizes, and maintains data architecture and pipelines that adhere to ELT principles and business goals.
 Solves complex data problems to deliver insights that helps business achieve its goals.
 Creates data products for engineers, analysts, and data scientist team members to accelerate their productivity.
 Engineer effective features for modelling in close collaboration with data scientists and businesses.
 Leads the evaluation, implementation and deployment of emerging tools and process for analytics data engineering to improve productivity and quality.
 Partners with machine learning engineers, BI, and solutions architects to develop technical architectures for strategic enterprise projects and initiatives.
 Fosters a culture of sharing, re-use, design for scale stability, and operational efficiency of data and analytical solutions.
 Advises, consults, mentors, and coach other data and analytic professionals on data standards and practices.
 Develops and delivers communication and education plans on analytic data engineering capabilities, standards, and processes.
 Learns about machine learning, data science, computer vision, artificial intelligence, statistics, and/or applied mathematics as necessary to carry out role effectively.
Required Skills:
 5-10 years of experience required.
 Experience with designing and maintaining data warehouses and/or data lakes with big data technologies such as Spark/Databricks, or distributed databases, like Redshift and Snowflake, and experience with housing, accessing, and transforming data in a variety of relational databases.
 Experience in building data pipelines and deploying/maintaining them following modern DE best practices (e.g., DBT, Airflow, Spark, Python OSS Data Ecosystem).
 Knowledge of Software Engineering fundamentals and software development tooling (e.g., Git, CI/CD, JIRA) and familiarity with the Linux operating system and the Bash/Z shell.
 Experience with cloud database technologies (e.g., Azure) and developing solutions on cloud computing services and infrastructure in the data and analytics space.
 Basic familiarity with BI tools (e.g., Alteryx, Tableau, Power BI, Looker).
 Expertise in ELT and data analysis, SQL primarily.
 Conceptual knowledge of data and analytics, such as dimensional modelling, reporting tools, data governance, and structured and unstructured data.