Data engineer | Kenility

Data engineer (POS-143)

About Us:

As a Senior Data Engineer at Kenility, you’ll join a tight-knit family of creative developers, engineers, and designers who strive to develop and deliver the highest quality products into the market.

Technical Requirements:

Bachelor’s degree in Computer Science, Software Engineering, or a related field.
Extensive experience with Snowflake, including schema optimization, query performance tuning, and use of advanced features like micro-partitioning, zero-copy cloning, and time travel.
Solid background with Amazon Redshift, including architecture understanding, performance tuning, and migration strategies to Snowflake.
Strong command of Microsoft SQL Server for legacy data management, complex SQL scripting, and stored procedure development.
Advanced SQL capabilities for cross-platform querying, data transformation, and issue resolution.
Skilled in designing both normalized and denormalized data models optimized for cloud-based analytics.
Proven experience in building and managing orchestration workflows using tools like Apache Airflow.
Deep understanding of modern data ingestion and transformation approaches, with hands-on ELT/ETL pipeline optimization.
Demonstrated ability to integrate and orchestrate diverse data sources for reliable data workflows.
Committed to enforcing high standards for data quality, including validation, monitoring, and error handling.
Practical experience implementing CI/CD pipelines for data projects using tools like Jenkins or Bitbucket.
Effective collaboration in Agile environments, working closely with multidisciplinary teams to meet iterative goals.
Strong problem-solving mindset with the ability to propose and implement innovative solutions.
Minimum Upper Intermediate English (B2) or Proficient (C1).

Tasks and Responsibilities:

Lead the transition of datamarts from Amazon Redshift and Microsoft SQL Server to Snowflake with minimal disruption and enhanced performance.
Plan and execute the phased decommissioning of outdated datamarts while ensuring data integrity.
Design scalable and efficient Snowflake-based data models to support robust analytical capabilities.
Refactor legacy pipelines to adopt modern ELT strategies for improved maintainability and speed.
Build and manage data workflows using orchestration tools to automate ingestion and transformation processes.
Define and uphold validation standards to guarantee consistent, high-quality data.
Integrate CI/CD methodologies to streamline development and deployment of data solutions.
Collaborate in Agile teams alongside engineers, analysts, and stakeholders to deliver incremental improvements.
Identify inefficiencies in data systems and implement enhancements to boost performance.
Maintain detailed documentation of architecture, pipelines, and processes to facilitate team knowledge sharing.