Senior Software Engineer - Data Engineering
Oracle
The Senior Data Engineer / Analyst is a hands-on role that blends deep technical expertise in modern data engineering with strong analytical and consulting skills. This position focuses on building and maintaining real-time data pipelines, implementing scalable data mesh architectures, and developing analytical solutions that drive business insights and enable data-driven decision-making.
As part of a world-class data team, you will design and model data solutions to support analytics and machine learning use cases, ensuring data is accurate, accessible, and actionable. The role involves developing greenfield data products, partnering with cross-functional teams to translate business needs into technical solutions, and delivering measurable business outcomes across diverse environments.
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Senior Software Engineer - Data Engineering
Career Level - IC3
Core Technical Responsibilities
- Developing Data Solutions: Implement and enhance data-driven solutions to integrate massive amounts of data in real-time/batch mode from various data producing systems using state-of-the-art tools such as OCI Data Flow, OCI Data Integration, Spark and Kafka. Embrace modern data architecture philosophies including data products, data contracts, and data mesh to ensure a decentralized approach to data management. Expert in Advanced SQL and PL/SQL for data analysis and testing data gaps & data quality
- Data Pipeline Development: Develop and optimise high-performance, batch and real-time data pipelines employing advanced streaming technologies like Kafka, NoSQL and Oracle ETL Tools to address challenges associated with large-scale data processing and analysis.
- Cloud Data Management: Implement and oversee cloud-specific data services including Autonomous Data Warehouse, OCI Object Storage with Parquet and Delta-Tables and OCI Streams. Leverage cloud architectures to improve data sharing and interoperability across different business pillars
- Security and Compliance: Ensure all data practices comply with security policies and regulations, embedding security by design in the data infrastructure. Incorporate tools and methodologies recommended for data security and compliance, ensuring robust protection and governance of data assets. Ensure data governance, security, and compliance: lineage, cataloguing, DQ checks, PII protection, and privacy‑by‑design
Ideal Skills and Experience
We use a broad range of tools, languages, and frameworks. We don’t expect you to know them all but experience or exposure with some of these (or equivalents) will set you up for success in this team
- Core Data Engineering Tools & Technologies: Demonstrates proficiency in SQL and Spark, and familiarity with platforms for Big Data and Data Lakehouses. Well-versed in various technologies including ETL/ELT, NoSQL and Advanced SQL/PL-SQL to work with unstructured JSONs. Adept in various modern data-architecture patterns
- Data Storage Expertise: Knowledgeable in data warehousing technologies and proficient in managing various data storage formats including Parquet, Delta, ORC, Avro, and JSON to optimise data storage and retrieval
- Data Modelling Expertise: Proficient in data modelling, understanding the implications and trade-offs of various methodologies and approaches
- Infrastructure Configuration for Data Systems: Competent in setting up data system infrastructures, favouring infrastructure-as-code practices using tools such as Terraform
- Programming Languages: Proficient in Python and Advanced SQL
- CI/CD Implementation: Knowledgeable about continuous integration and continuous deployment practices using tools like GitHub enhancing software development and quality assurance
- Agile Delivery and Project Management: Skilled in agile and kanban project delivery methods, ensuring efficient and effective solution development
- Communication Skills: Effective in engaging stakeholders and translating business requirements into practical data engineering strategies. Collaborate with cross‑functional teams to ensure solutions meet performance, reliability, and operational excellence standards
Professional Experience and Qualifications
- Professional Experience: At least 8+ years of data engineering or equivalent experience in a commercial, enterprise, or start-up environment. Consulting experience within a technology consultancy or professional services firm is highly beneficial.
- Are passionate about building next generation data platforms and data pipeline solution across the bank.
- Constantly thinking outside the box and breaking boundaries to solve complex data problems.
- Can collaborate, co-create and contribute to existing Data Engineering practices in the team.
- Excited to run data projects independently end to end - from Data Analysis to deployment, utilising the DevOps/DataOps model and experiment with new technologies.
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