We are looking for a seasoned Data Engineering Architect with a profound expertise in data modeling and big data technologies. This role is pivotal in designing, implementing, and maintaining sophisticated data models and ETL processes that are crucial for enhancing our data analytics capabilities and supporting core business functions. The ideal candidate will leverage their extensive experience to build scalable and robust data solutions.

This position demands a strategic approach to data architecture, focusing on optimizing performance and ensuring data integrity across large-scale datasets. You will be instrumental in shaping our data infrastructure and driving innovation in how we manage and utilize data.

Design, develop, and optimize comprehensive data models, including conceptual, logical, and physical layers, ensuring data integrity and peak performance. Utilize PySpark for advanced large-scale data processing and transformation. Implement and manage big data solutions on platforms like Hadoop, Spark, and Hive, while continuously optimizing processing pipelines.

Develop and maintain efficient, scalable, and reliable ETL processes for ingesting data from diverse sources. Collaborate closely with data engineers, data scientists, and stakeholders to ensure seamless data integration. Document data models, ETL pipelines, and processes thoroughly for knowledge sharing and future reference. Identify and implement performance tuning opportunities for data models and ETL workflows, monitoring system performance to proactively address issues.

A Bachelor’s degree in Computer Science, Information Technology, Data Science, or a related field is required. You should possess 3+ years of experience in data modeling and database design, coupled with 3+ years of hands-on experience in big data technologies such as PySpark, Hadoop, Spark, and Hive. Proven expertise in ETL tools and processes is essential.

Technical proficiencies include strong SQL skills, experience with relational databases (MySQL, PostgreSQL, Oracle), and advanced Python programming, especially with PySpark. Familiarity with data warehousing solutions like Redshift or Snowflake is expected. Experience with cloud platforms (AWS, Azure, GCP), Continuous Integration tools (Jenkins, Git), and graph processing technologies (GraphX, Neo4j) are considered advantageous. Strong analytical, problem-solving, and communication skills are vital.