Java Software Engineer

Data Engineer

About the Role

Digital products play a central role in how we create value for customers, support the teams who serve them, and shape the consumer experience. We are seeking a Data Engineer to design, build, and maintain scalable data solutions that power analytics, reporting, machine learning, and data-driven decision-making.

In this role, you will work closely with Product, Analytics, Data Science, and Engineering teams to develop trusted datasets, reliable data pipelines, and scalable data platforms. You will help transform raw data into high-quality, actionable information while ensuring performance, quality, governance, and operational excellence.

What You'll Do

• Data Engineering: Design, build, and maintain scalable data pipelines, data models, and data products using Java, Python, SQL, and cloud-native data engineering patterns.
• Software Engineering Practices : Apply strong coding standards, modular design, automated testing, version control, CI/CD, and code review practices to data engineering solutions.
• Azure Data Platform Delivery : Build and deploy data workloads on Azure platforms such as ADLS, Azure Data Factory, Synapse, Databricks, Microsoft Fabric, and related services as applicable.
• Production Operations : Implement reliable deployment automation, monitoring, logging, alerting, and operational controls to keep data pipelines and services stable in production.

• Collaborate with platform teams to improve tooling, automation, CI/CD, monitoring, and observability.
• Utilize version control and software engineering best practices to maintain high-quality code.

Collaboration & Innovation

• Partner with Product Managers, Analysts, Data Scientists, and Software Engineers to deliver business value.
• Contribute to data engineering standards, design reviews, and documentation.
• Continuously identify opportunities to improve reliability, scalability, and efficiency across the data ecosystem.

Required Qualifications

• Bachelor's degree in computer science, Engineering, Information Systems, or a related field.
• 3+ years of experience in Data Engineering, Analytics Engineering, Software Engineering, or related disciplines.
• Programming: Strong proficiency in Python and SQL, with familiarity in Java or other backend programming languages preferred.
• Data Engineering Expertise: Proven experience building batch and streaming data pipelines, transformation frameworks, data models, and reusable data engineering components.
• Cloud Data Platforms: Hands-on experience with Azure data services such as ADLS, ADF, Synapse, Databricks, Microsoft Fabric, or equivalent cloud data platforms.
• Software Engineering Discipline: Strong understanding of design patterns, modular coding, error handling, automated testing, CI/CD, Git-based workflows, and production-grade engineering practices.
• Data Quality & Observability: Experience implementing data validation, monitoring, logging, lineage, and alerting to ensure trusted and reliable data products.
• DevOps & Deployment: Familiarity with Git, CI/CD pipelines, containerization concepts, and deployment automation for data engineering workloads.
• .

Preferred Qualifications

• Experience with Azure Synapse, Databricks, Snowflake, BigQuery, Redshift, or similar platforms.
• Experience with orchestration tools such as Airflow, Synapse Pipelines, Dagster, or dbt.
• Familiarity with CI/CD, Infrastructure as Code, and DevOps practices.
• Experience supporting machine learning workflows through feature engineering and training data preparation.
• Exposure to data observability, monitoring, testing, and governance tools.
• Knowledge of event-driven architecture and real-time data processing.
• Azure Data Engineer or equivalent cloud certification.

Success Attributes

The ideal candidate:

• Demonstrates strong ownership and accountability.
• Passionately advocates for data quality, reliability, and trust.
• Communicates effectively with both technical and non-technical stakeholders.
• Balances technical excellence with practical business outcomes.
• Thrives in a collaborative, agile environment.
• Continuously seeks opportunities to learn and improve processes.

Key Technologies

Programming: Python, SQL, Scala, Java
Cloud: Azure, AWS, Google Cloud Platform
Data Platforms: Databricks, Snowflake, Synapse, Redshift, BigQuery
Data Processing: Spark, dbt
Orchestration: Airflow, Synapse Pipelines, Dagster
DevOps: GitHub, GitHub Actions, CI/CD
Data Modeling: Dimensional Modeling, Data Warehousing, Lakehouse Architecture