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Role Overview

The Data Engineer is responsible for designing, building, and operating scalable, reliable, and secure data pipelines and data platforms that power digital products, analytics use cases, reporting, and data-driven services. Acting as a key contributor within cross-functional delivery teams, the role collaborates closely with Product, Architecture, Data Analytics, and platform teams to deliver trusted data assets across ingestion, transformation, storage, and access layers.

At Senior Associate level, the role independently owns complex pipelines and data services while improving quality, performance and platform reliability.

Key Responsibilities

• Own complex batch or streaming pipelines from design through production support, coordinating dependencies and resolving data-quality and performance issues independently.
• Contribute to data architecture, reusable frameworks, code reviews, observability improvements and the coaching of less-experienced engineers.

Data Pipeline & Platform Delivery

• Design, implement, and maintain batch and streaming data pipelines using Python and modern data engineering frameworks.
• Build reliable ingestion and transformation processes from internal and external data sources, including APIs, databases, files, and events.
• Develop reusable data processing components and frameworks following software engineering best practices.

Data Quality, Reliability & Observability

• Implement data validation, quality checks, and monitoring to ensure accuracy, completeness, timeliness, and trustworthiness of data.
• Write clean, testable code with unit and integration tests for data pipelines and processing components.
• Instrument pipelines with logging, metrics, alerts, and operational monitoring.

Performance, Scalability & Architecture

• Optimize data processing performance, storage layouts, query efficiency, and platform resource usage.
• Design pipelines for scalability and resilience using retries, idempotency, checkpointing, and backpressure patterns.
• Apply partitioning, caching, and parallelization strategies where appropriate.

Security, Governance & Continuous Improvement

• Implement secure data handling practices, including access controls, encryption, secrets management, and compliant data processing.
• Ensure alignment with enterprise, regulatory, privacy, data classification, retention, and lineage requirements.
• Support audits and data governance processes where required.

What success in this role will look like

• Demonstrates independent ownership of complex data solutions, strong technical judgement and measurable improvement in trust, performance and reliability.
• Reliable delivery and operation of scalable data pipelines and data platform components.
• Trusted data assets with strong accuracy, completeness, timeliness, and observability controls.
• Clean, testable, documented, and maintainable Python and SQL-based data engineering solutions.
• Effective support for incidents, troubleshooting, root-cause analysis, and durable remediation.
• Provide mentorship and guidance to less experienced team members.

Qualifications & Experience

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, Information Technology, or a related field.
• Professional experience building data pipelines and data platforms using Python.
• Strong experience with SQL and data modeling concepts, including dimensional, normalized, analytical, or lakehouse patterns.
• Hands-on experience with relational and analytical data stores, such as PostgreSQL, SQL Server, data warehouses, or data lakes.
• Experience with CI/CD, automated testing, and code reviews for data workloads.
• Advance knowledge of cloud platforms, with Azure/Fabric, and relevant data services.
• Work effectively as part of a team, contributing to collective success while remaining open to feedback and coaching from more experienced colleagues.
• Strong understanding of data security, access control, observability, and production support practices.
• 4+ Years of Experience are required.

Preferred Experience

• Experience with modern data frameworks and tools such as Spark, Airflow, dbt, Azure Data Factory, Microsoft Fabric, Databricks, or equivalent platforms.
• Exposure to streaming and event-based data processing using Kafka, Azure Event Hubs, Service Bus, or similar technologies.

Advanced experience with lakehouse or warehouse platforms

What we offer you

At EY, we’ll develop you with future-focused skills and equip you with world-class experiences. We’ll empower you in a flexible environment, and fuel you and your extraordinary talents in a diverse and inclusive culture of globally connected teams. Learn more.

Are you ready to shape your future with confidence? Apply today.

To help create the best experience during the recruitment process, please describe any disability-related adjustments or accommodations you may need.

 EY  |  Building a better working world

EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.

Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.

EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.

Our offer of employment is contingent upon the successful completion of a background check and pre-screening requirements. The candidate acknowledges that all information provided must be accurate.