Data Engineer - Financial Data Platform

The Team:

The Financial Platform Group is part of our Data & Analytics org within Finance Planning and Analytics (FP&A). Our team acquires, processes, cleanses, and stores internal company data from multiple business systems in a central, cloud-based data environment. As a Data Engineer, you will work closely with a small, distributed team of Analysts, Analytics Engineers, and Data Engineers to develop, operate, and support our modern lakehouse data platform. This role offers the opportunity to design and implement scalable data infrastructure and pipelines to unlock insights from our data and power our Celonis Process Intelligence Platform. 

The Role:

As a Data Engineer on the Financial Data Platform team, your primary responsibility will be designing, building, and maintaining the data infrastructure that powers our Celonis Process Intelligence Platform.

You will collaborate with a distributed group of Analysts, Analytics Engineers, and fellow Data Engineers to develop scalable, reliable data pipelines and processes that transform our internal company data into valuable insights. This role offers the opportunity to significantly impact our data architecture while continuously enhancing our modern data stack.

The work you’ll do:

• Develop and optimize data ingestion pipelines: Use data integration services to extract data from various source systems and load it into targets, such as Catalogs, Lakes, Warehouses, Execution Management Systems, and other Business Systems.
• Expand data coverage: Integrate with APIs, databases, and custom sources to ensure reliable and efficient data ingestion and broaden the scope of the data you work with.
• Design scalable data processing jobs: Implement ELT patterns and data frameworks to transform raw source data into refined, analysis-ready datasets.
• Ensure data quality and observability: Build data quality checks, manage dependencies, and maintain pipeline health using asset-based orchestration tools that provide full end-to-end observability.
• Build event-driven microservices: Create efficient processes for handling data transformations and processing tasks.
• Collaborate with analytical stakeholders: Partner with analysts and analytics engineers to understand data requirements, model data effectively in the Data Warehouse, and ensure pipelines deliver timely, complete data.
• Ensure data security and compliance: Implement access controls, encryption, and auditing mechanisms to safeguard our data.
• Monitor and troubleshoot pipelines: Proactively monitor the health of data pipelines, diagnose issues, and implement solutions to minimize downtime and data delays.
• Adopt and implement new technologies: Continuously evaluate emerging tools and technologies to enhance the efficiency and scalability of our data stack.

The qualifications you need:

• Degree in Computer Science, Engineering, Informatics, or a related technical field.
• 3+ years of experience as a Data Engineer, designing and operating data infrastructure and pipelines in the cloud, preferably on AWS.
• Strong proficiency in object-oriented Python and SQL for data processing and pipeline development.
• Experience with data orchestration tools like Dagster, Airflow, Prefect, or Luigi.
• Familiarity with data lake and lakehouse technologies such as Apache Iceberg, Apache Spark, and AWS Services such as Athena, EMR, Glue, and S3.
• Exposure to stream processing and event-driven architectures using AWS Lambda or similar tools.
• Knowledge of data warehousing and modeling techniques, especially in Redshift. Data Vault 2.0 is a plus.
• Excellent problem-solving and communication skills, with the ability to translate business requirements into technical solutions.
• Passion for building scalable and reliable data systems to drive business impact.