GCP Data Engineer

Responsibilities

• Demonstrates passion, excellence, trust, transparency, and client focus.
• Buils and maintains databases, data pipelines, and data lakes.
• Uses Dataplex to catalog, govern, and manage data quality across data lakes and data warehouses.
• Transforms and models data, develops reporting and analytics, creates dashboards, and works with analytics tools.
• Efficiently, effectively, and carefully leverages AI tools to develop data platforms and data analytics solutions.
• Understands business problems and processes based on direct conversations with clients, can see the big picture, and translate that into specific solutions.
• Collaborates with other technical experts on the DADP team and is involved in all parts of the data analytics development process—from gathering requirements and solution delivery to documentation of lessons learned.
• Approaches conflict directly, assumes positive intent, and seeks assistance from leaders if needed.
• Adapts positively to change by embracing new practices, processes, or circumstances.
• Presents a positive, professional, and self-confident image.
• Responds to clients in a timely and professional manner.
• Understands and strives to consistently meet or exceed client expectations.
• Collaborates with clients to identify and address their business needs.
• Communicates accurately, concisely, and effectively.
• Respectfully listens to others’ ideas and concerns with an open mind.
• Develops and maintains effective working relationships with others.
• Works collaboratively to solve problems and puts team success first.
• Maintains top-notch service and suggestions to not only clients and the project team but also to the broader NRI team.

Qualifications

• 3-5 years of work experience in data engineering.
• Your data engineering experience should include Google Cloud Platform (GCP) technologies and be familiar with modern data platform technologies such as BigQuery, Dataflow (Apache Beam), Dataproc (managed Spark/Hadoop), Cloud Composer (managed Apache Airflow), Cloud Data Fusion, Looker / Looker Studio.
• Deep familiarity and experience in the following areas: Data warehouse and lake house methodologies, including medallion architecture. Data ETL/ELT processes. Data profiling and anomaly detection. Data modeling (Dimensional/Kimball).
• A strong background in relational database platforms.
• DevOps/Continuous integration & continuous delivery.
• Excellent analytical and critical thinking skills.
• Effective oral and written communication and organizational skills.
• Desire for continued education and certification as it relates to the position.
• Adaptability to diverse business environments.

Preferred

• A previous consulting experience is preferred but not required.
• Experience migrating data platforms and workloads from Microsoft Azure to Google Cloud Platform (GCP).
• Google Cloud Professional Data Engineer certification (or equivalent GCP certification).