
Highly Skilled Data Architect Lead
1 day ago
At Auctane Poland Sp. z o.o., we are seeking a highly skilled Senior Data Engineer to join our Data Products team. This is a hands-on role where you will have an enormous impact on the design, implementation, and maintenance of existing and greenfield customer-facing data products.
About the RoleThis role requires a strong Data Engineering background in a data warehouse or data lake architecture. You should have experience working in AWS/GCP cloud infrastructure, as well as developing and supporting robust, automated, and reliable data pipelines in Python and SQL.
We are looking for someone who is proficient in data processing frameworks like Spark, Athena, or Pandas, and has knowledge of Data Orchestration solutions, preferably Airflow. Experience with relational database design as well as Snowflake/Redshift or similar is also a plus.
In this role, you will contribute directly to the implementation and operations of our systems, driving the design and architecture of Auctane's data product, and be an advocate of data quality and observability principles. You will also foster engineering excellence by delivering highly reliable software and data pipelines using Software Engineering best practices.
Key Responsibilities- Contribute to the design, build, and operational management of our data pipelines and analytics solution on top of proven AWS data technologies like S3, Athena, Lambda, and Kinesis.
- Collaborate with the Product Owner and other stakeholders to implement the Data Products strategy.
- Develop frameworks and solutions that enable us to acquire, process, monitor and extract value from our massive dataset.
- Drive the design and architecture of Auctane's data product.
- Be an advocate of data quality and observability principles and use state-of-the-art technologies like Airflow, dbt, and Spark to process data and get our datasets just right.
- Foster engineering excellence by delivering highly reliable software and data pipelines using Software Engineering best practices.
- Data engineering
- AWS, GCP, Cloud
- Data pipelines, SQL, Python, Spark, Athena, pandas, Airflow
- Relational database, Snowflake, Redshift
- AWS EMR, Glue, EC2
- Infrastructure as Code, Terraform, CloudFormation
- Tableau, Looker, Power BI, dbt
- Sport subscription
- Training budget
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