Data Engineer
About Apexon:
Apexon brings together distinct core competencies – in AI, analytics, app development, cloud, commerce, CX, data, DevOps, IoT, mobile, quality engineering and UX, and our deep expertise in BFSI, healthcare, and life sciences – to help businesses capitalize on the unlimited opportunities digital offers. Our reputation is built on a comprehensive suite of engineering services, a dedication to solving clients’ toughest technology problems, and a commitment to continuous improvement.
Backed by Goldman Sachs Asset Management and Everstone Capital, Apexon now has a global presence.
Role Overview
We are seeking a highly skilled Data Engineer with strong Python and cloud expertise to join the Data Engineering team of a leading investment bank. This role focuses on designing and building scalable, secure, and auditable data platforms that enhance audit analytics, automation, and insight generation. You will play a critical role in developing cloud-based data applications, APIs, and pipelines to support audit processes, risk assessments, and regulatory requirements. The ideal candidate will combine strong engineering skills with an understanding of data governance, controls, and financial services environments.
Key Responsibilities
- Design, develop, and maintain scalable data pipelines and workflows for audit data ingestion, transformation, and reporting.
- Build and deploy cloud-native data applications to support audit analytics and monitoring.
- Develop and maintain robust APIs for data access, integration, and audit tooling.
- Collaborate with audit teams to translate business requirements into technical data solutions.
- Implement data quality, validation, and lineage frameworks aligned with audit and compliance standards.
- Support real-time and batch data processing architectures.
- Ensure solutions adhere to security, governance, and regulatory requirements.
- Contribute to automation of audit processes through data engineering and analytics.
- Work closely with IT, risk, and compliance teams on data-related initiatives.
Mandatory Technical Skills
- Python (Core Requirement)
- Strong, hands-on experience in Python for building production-grade data solutions, including:
- Developing data pipelines and ETL/ELT processes using frameworks such as Airflow, Prefect, or similar
- Building RESTful APIs using frameworks like FastAPI, Flask, or Django
- Writing efficient, scalable, and maintainable code following best practices
- Working with data processing libraries such as Pandas, PySpark, and NumPy
- Implementing data validation, logging, and error handling mechanisms
- Experience with asynchronous programming and performance optimization
- Building microservices-based architectures
- Cloud Technologies
- Proven experience with at least one major cloud platform (AWS, Azure, or GCP)
- Building and deploying cloud-based data applications
- Experience with:
- Cloud data services (e.g., data lakes, warehouses)
- Serverless architectures
- Containerization (Docker, Kubernetes)
- Strong understanding of cloud security, IAM, and networking
- Data Engineering & Pipelines
- Experience designing and maintaining scalable data pipelines
- Knowledge of streaming and batch processing systems
- Familiarity with data modeling, schema design, and optimization
- Experience with SQL and NoSQL databases.
- Experience with On-Premise to Cloud migration, including:
- Data platform modernization
- Migration of legacy ETL pipelines to cloud-native solutions
- Hybrid architecture design and implementation
- Proven track record in building enterprise-grade data platforms
Additional Skills (good to have)
- Familiarity with data governance, lineage, and audit frameworks
- Knowledge of DevOps and CI/CD pipelines
- Experience with big data technologies (e.g., Spark, Hadoop)
- Understanding of risk and control frameworks within banking
- Exposure to machine learning pipelines is a plus
- Experience working in financial services, investment banking, or regulated environments preferred.
Soft Skills
- Strong analytical and problem-solving abilities
- Ability to communicate complex technical concepts to non-technical stakeholders
- High attention to detail, especially in regulated environments
- Collaborative mindset with strong stakeholder engagement skills