Data Engineer | Kenility

Data Engineer (P848)

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.
Advanced SQL skills for querying, managing, and optimizing relational databases, with the ability to handle complex queries and large volumes of data efficiently.
Strong command of Python, particularly in the use of data processing libraries like Pandas, for building scalable and maintainable scripts and automation tools.
Solid experience with PySpark for distributed processing of large-scale datasets, including familiarity with Spark’s DataFrame API, RDDs, and performance tuning techniques.
In-depth knowledge of ETL methodologies and proven ability to design and implement efficient data pipelines ensuring high data quality.
Skilled in data modeling practices, including the creation of logical and physical schemas aligned with reporting and querying efficiency.
Experience working with RDBMS platforms such as Oracle or MySQL, with an understanding of database design, indexing, and query performance optimization.
Understanding of data warehousing principles and experience in building data warehouse solutions tailored for analytics and business intelligence.
Proficiency in Unix/Linux systems for workflow management, automation scripting, and system operations.
Capable of writing shell scripts to streamline data processing tasks and support integration within the system infrastructure.
Familiarity with implementing automated testing strategies for ETL processes to safeguard data integrity and system reliability.
Minimum Upper Intermediate English (B2) or Proficient (C1).

Tasks and Responsibilities:

Design, implement, and maintain scalable ETL workflows and data pipelines that support seamless data ingestion, transformation, and storage.
Develop automated systems for validating and monitoring data quality across platforms, ensuring accuracy and consistency.
Collaborate with cross-functional teams to translate business and analytical requirements into robust technical solutions.
Monitor data infrastructure performance and proactively optimize for scalability, reliability, and cost-effectiveness.
Troubleshoot and resolve issues related to data systems, providing thorough analysis and implementing long-term fixes.
Maintain detailed documentation of pipeline architectures, processes, and system components, while actively contributing to code and design reviews.
Keep pace with emerging trends and tools in data engineering to introduce improvements and innovation into existing workflows.
Support the professional growth of junior team members through mentoring and technical guidance.