ll Be Doing

Design and architect complex, scalable, and reliable data pipelines and data warehouses.
Lead the development and maintenance of ETL/ELT processes for high-volume, real-time, and batch data.
Spearhead the adoption of new technologies and methodologies to improve data platform efficiency and performance.
Mentor and guide a team of data engineers, fostering a culture of technical excellence and innovation.
Collaborate closely with data scientists, analysts, and business stakeholders to define data strategy and requirements.

What We'd Love To See

5 - 10 years of experience in data engineering, with a proven track record of designing and implementing large-scale data solutions.
Expert-level proficiency in SQL and at least one programming language (Python, Scala, or Java).
Deep knowledge of big data technologies like Spark, Kafka, and distributed systems.
Extensive experience with cloud data platforms (AWS, Azure, GCP) and data warehousing solutions (Snowflake, BigQuery, Redshift).
Demonstrated experience in leading projects and mentoring junior team members.

It'd Be Great If You Had

1a:T570,

Designing Data Models: Utilize Kimball data modeling techniques and Data Vault 2.0 methodologies to create and maintain robust data models.
Data Integration: Use Fivetran for seamless data extraction and loading, and Azure Data Factory (ADF) for orchestrating data workflows.
Data Warehousing: Manage and optimize data storage in Snowflake, ensuring efficient data retrieval and processing.
Data Transformation: Develop and maintain transformation scripts using DBT (Data Build Tool) to convert raw data into actionable insights.

Sr. Data Engineer