Azure Data Engineer

Senior Azure Data Engineer

Job Description:

Cleans, prepares, and optimizes data for further analysis and modeling.

Designs, develops, optimizes, and maintains data architecture and pipelines that adhere to Data Pipeline (ELT) principles and business goals.

Essential Job Functions / Principal Accountabilities:

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 help the business achieve its goals.

Creates data products for engineers, analysts, and data scientist team members to accelerate their productivity.

Engineers effective features for modeling in close collaboration with data scientists and businesses.

Leads the evaluation, implementation, and deployment of emerging tools and processes 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 coaches 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 the role effectively.

Minimum Skills and Qualification Requirements:

Bachelors degree in computer science, statistics, engineering, or a related field.

8+ years of experience required.

Experience in PySpark is required for this position, with strong knowledge of Microsoft Fabric being a plus.

Experience designing and maintaining data warehouses and/or data lakes with big data technologies such as Spark/Databricks or distributed databases like Redshift and Snowflake.

Experience in housing, accessing, and transforming data in various relational databases.

Experience building data pipelines and deploying/maintaining them following modern Data Engineering 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), with 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, primarily SQL.

Conceptual knowledge of data and analytics, such as dimensional modeling, reporting tools, data governance, and structured and unstructured data.