Data Engineer Central Europe

Zoolatech; Central, Northern (LA, KY); Hybrid; USD 110,000 - 150,000; Full time

Job summary

Zoolatech is seeking a Data Engineer to help transform its analytics footprint, migrating from traditional data solutions to Azure-based services.

You'll partner with product owners and architects to build scalable data products, design data pipelines, and enable data-driven decision making across the organization. Strong Python, SQL, and cloud experience are essential, as is DevOps and CI/CD know-how.

Qualifications

• 5+ years of experience as a Data Engineer or software engineer with data focus.
• Experience building scalable data products on cloud platforms (Azure/AWS).
• Proficiency in SQL and relational datasets; data warehousing and dimensional modeling.

Responsibilities

• Build and implement scalable data products on the global data platform.
• Design scalable, efficient, and stable data products with a product team.
• Deliver features for reporting and analytics to drive business value.
• Own data pipelines and ensure stability and value realization.
• Collaborate with stakeholders to turn requirements into technical design.

Skills

Python programming

SQL

CI/CD

Agile/Scrum

Data modeling

Communication

Education

Bachelor's degree in Computer Science, Math, Economics, Information Management, or Statistics

Tools

ADF

Data Lake

Synapse

TU-SQL

Databricks

MDS

DAX

SSIS

SQL Server

Airflow

Kubernetes

Helm

Kafka

Spark Streaming

Git

CI/CD tools

Job description

Our client is a global jewelry manufacturer that is transforming its data and analytics footprint and is also on a journey from IaaS-based Microsoft/QV solutions to Azure PaaS.

As a Data Engineer, you will collaborate with product owners, architects, and other key profiles across the global organization. You will help business verticals and clusters maximize the value of the data and drive business change as a result.

• Your primary focus will be building and implementing scalable data products on the global data platform, alongside other smart engineers in the organization.
• Work closely with your colleagues within a product team to design scalable, efficient, and stable products
• Work closely with product owners to build and deliver features designed to drive commercial business cases and efficiencies within reporting and analytics, and other data-driven use cases
• Create and maintain a high-quality data pipeline architecture
• Obtain relevant business and system knowledge to convert requirements efficiently to technical design documentation and conceptual data modelling
• Own your data products and track continuous stability and benefits
• Live and breathe "build-once-consume-many" culture in which we enable others to use our data products for maximum value-add
• Work with stakeholders and teams to assist with data-related technical challenges
• Follow best practices and existing guidelines, but also push the limits
• Thrive in a fast-paced environment, and you get motivated by challenges as a true problem solver
• Relevant education within Computer Science, Mathematics, Economics, Information Management, or Statistics
• 5+ years of experience as a Data Engineer or in a Software engineering role with a focus on data
• Proven experience with ADF, Data Lake, Synapse, T/U-SQL, Databricks, MDS, DAX, SSIS, and SQL Server
• Experience with Python (not just scripting but programming)
• DevOps and CI/CD are must-haves
• Hands-on with git and coding best practices
• Unit testing
• Experience working with relational data sets using SQL
• Experience with data warehousing, dimensional modelling, and cubes
• Experience in building and optimizing data pipelines, architectures, and datasets
• Experience in working with Azure, AWS, or other cloud providers
• Designed, built, and delivered production-ready data products at an enterprise level
• Highly proficient in English and able to communicate with all levels
• Agile mindset and experience with Scrum

It's a plus if you have worked with:

• Big data: Spark, Hadoop
• Data pipeline and workflow tools: Airflow
• Infrastructure: Kubernetes and Helm; we're using Airflow running on Kubernetes
• Stream-processing systems: Kafka or Spark Streaming