AWS Data Engineer – Python, Databases, Capital Markets & DevOps

Job Summary

Synechron is seeking an AWS Data Engineer with a 70% focus on data engineering and a 30% focus on DevOps to design, develop, deploy, and support scalable data solutions on AWS.

The role requires strong AWS knowledge, advanced Python development skills, robust database expertise, and an understanding of capital markets, including Fixed Income instruments and related financial concepts. The AWS Data Engineer will work with relational, columnar, and vector databases while supporting cloud infrastructure, CI/CD processes, and reliable application delivery.

This position contributes to business objectives by delivering secure and scalable data solutions, improving database performance, supporting capital markets capabilities, strengthening deployment processes, and enabling reliable access to high-quality data.

Software Requirements

Required Software Skills

- AWS services and cloud infrastructure: Proven experience designing, developing, deploying, and supporting applications and data solutions on AWS.
- Python: Strong professional experience writing clean, efficient, reliable, maintainable, and testable Python code.
- Relational databases: Extensive experience with relational database technologies, including MySQL and PostgreSQL.
- Columnar databases and data formats: Experience with Amazon Redshift and Apache Parquet.
- Vector databases: Experience working with vector databases to support project requirements, search, analytics, or data retrieval.
- Database design and optimization: Knowledge of schema design, indexing, query optimization, data modeling, and database architecture.
- Data engineering: Experience developing data solutions, managing data flows, and working with structured and semi-structured data.
- DevOps: Ability to support cloud deployments, infrastructure activities, CI/CD processes, troubleshooting, and operational reliability.
- Capital markets: Knowledge of capital markets, including Fixed Income instruments and related financial concepts.
- Software development practices: Knowledge of software development best practices, design principles, testing, version control, and technical documentation.

Preferred Software Skills

- AWS certifications relevant to data engineering, cloud architecture, or DevOps.
- Experience with data modeling and data warehousing.
- Familiarity with DevOps tools and CI/CD pipelines.
- Experience with infrastructure automation and cloud deployment processes.
- Understanding of additional financial instruments and capital markets concepts.
- Experience supporting data-intensive applications in financial services or related environments.

Overall Responsibilities

- Develop and deploy scalable applications and data solutions on the AWS cloud platform.
- Write clean, efficient, reliable, and maintainable Python code for data processing, application services, automation, and operational support.
- Design, implement, maintain, and optimize relational, columnar, and vector databases.
- Apply database design and architecture best practices to support performance, scalability, availability, data quality, and maintainability.
- Develop data and application solutions related to capital markets and Fixed Income instruments.
- Build and support data models, schemas, data flows, data warehouse structures, and data access processes.
- Contribute to DevOps activities, including deployment automation, CI/CD pipelines, infrastructure support, monitoring, and release processes.
- Collaborate with cross-functional teams to define technical requirements, data requirements, solution designs, and delivery priorities.
- Troubleshoot and resolve application, data, database, cloud infrastructure, and deployment issues.
- Review technical designs and recommend improvements to application architecture, database performance, cloud usage, and operational processes.
- Apply secure development, access management, data protection, and operational control practices.
- Support efficient use of AWS compute, storage, database, and data processing resources to promote cost and environmental sustainability.

Performance Outcomes and Expectations

- AWS applications and data solutions are delivered in accordance with defined functional, technical, security, and operational requirements.
- Python code meets agreed standards for reliability, maintainability, testability, performance, and documentation.
- Database schemas, queries, data models, and storage structures support required performance and scalability.
- Relational, columnar, and vector database solutions are implemented appropriately for project needs.
- Data solutions accurately support capital markets and Fixed Income use cases.
- CI/CD and DevOps processes improve deployment consistency, traceability, and operational reliability.
- Technical issues are investigated systematically, resolved within agreed priorities, and documented for future reference.
- Cloud resources are used efficiently through appropriate architecture, monitoring, capacity planning, and optimization.

Technical Skills (By Category)

Programming Languages

Essential

- Strong Python programming skills for application development, data processing, automation, testing, and cloud-based solutions.
- Ability to write clean, efficient, reliable, modular, and maintainable code.
- Experience applying software development best practices, design principles, error handling, testing, and documentation.
- Ability to develop code that supports data-intensive applications and capital markets requirements.

Preferred

- Experience with additional programming or scripting languages used for data engineering, DevOps, or cloud automation.
- Experience developing reusable Python libraries, services, and automation frameworks.
- Experience with asynchronous, distributed, or event-driven application development.

Databases and Data Management

Essential

- Extensive knowledge of relational databases, including MySQL and PostgreSQL.
- Experience with columnar databases, including Amazon Redshift.
- Experience working with Apache Parquet and columnar data storage formats.
- Experience with vector databases.
- Knowledge of database schema design, normalization, partitioning, indexing, and query optimization.
- Understanding of data modeling, data quality, data lifecycle management, and data access patterns.
- Ability to select appropriate database technologies based on performance, scalability, query, storage, and project requirements.
- Understanding of data structures and data management practices relevant to capital markets and Fixed Income instruments.

Preferred

- Experience with data warehousing and analytical data models.
- Experience designing data lakes, data warehouses, or hybrid data platforms.
- Experience with data partitioning, compression, archival, and storage optimization.
- Experience with data lineage, metadata management, validation, and reconciliation.
- Experience supporting vector search and retrieval use cases.

Cloud Technologies

Essential

- Proven experience with AWS services and cloud infrastructure.
- Experience developing and deploying scalable applications and data solutions on AWS.
- Understanding of cloud computing concepts, including compute, storage, networking, databases, identity, monitoring, and resilience.
- Ability to troubleshoot AWS application, data, infrastructure, and deployment issues.
- Understanding of cloud resource management, availability, scalability, and cost considerations.

Preferred

- AWS certification relevant to data engineering, cloud architecture, or DevOps.
- Experience with infrastructure-as-code and automated environment provisioning.
- Experience with cloud monitoring, alerting, logging, and performance optimization.
- Experience with serverless, containerized, or event-driven AWS architectures.
- Experience applying cloud sustainability practices through resource optimization and efficient data processing.

Frameworks and Libraries

Essential

- Python libraries and frameworks relevant to data engineering, application development, database connectivity, testing, and automation.
- Libraries or tools required to work with relational, columnar, and vector databases.
- Frameworks and utilities that support data ingestion, transformation, validation, and processing.
- Technologies that support the development of capital markets and Fixed Income-related data solutions.

Preferred

- Experience with data processing and distributed computing frameworks.
- Experience with database migration, schema management, and data quality frameworks.
- Experience with libraries supporting financial data analysis, instrument reference data, pricing data, or risk-related use cases.
- Experience with frameworks for API development, workflow orchestration, and cloud automation.

Development Tools and Methodologies

Essential

- Knowledge of software development best practices and design principles.
- Experience with source control, code reviews, testing, release management, and technical documentation.
- Ability to contribute to DevOps activities and cloud deployment processes.
- Understanding of CI/CD concepts, automated testing, build processes, deployment controls, and environment management.
- Experience troubleshooting and resolving technical issues across application, database, cloud, and deployment layers.
- Ability to work independently and collaboratively in a team environment.

Preferred

- Familiarity with DevOps tools and CI/CD pipelines.
- Experience with automated infrastructure provisioning and configuration management.
- Experience with containerization and orchestration technologies.
- Experience with observability, logging, metrics, alerting, and operational dashboards.
- Experience with Agile development and iterative delivery practices.

Security Protocols

Essential

- Understanding of secure software development practices.
- Knowledge of AWS identity and access management, least-privilege access, authentication, and authorization.
- Understanding of database security, encryption, secrets management, and access controls.
- Ability to protect sensitive financial, application, and operational data.
- Awareness of secure coding, vulnerability management, auditability, and secure deployment practices.

Preferred

- Experience implementing security controls within CI/CD pipelines.
- Experience with cloud security monitoring and configuration assessment.
- Familiarity with data masking, tokenization, and privacy controls.
- Experience supporting applications subject to financial services, regulatory, or audit requirements.

Experience Requirements

- Minimum of 7–10 years of professional experience in data engineering, software engineering, cloud engineering, database engineering, DevOps, or related technology roles.
- Proven experience with AWS services, cloud infrastructure, Python development, and database engineering.
- Extensive experience with relational databases, including MySQL and PostgreSQL; columnar databases, including Amazon Redshift and Apache Parquet; and vector databases.
- Experience with database schema design, data modeling, query optimization, data warehousing, or related data architecture responsibilities.
- Demonstrated experience developing or supporting solutions related to capital markets, including Fixed Income instruments and related financial concepts.
- Experience with DevOps activities, CI/CD pipelines, deployment processes, troubleshooting, and operational support is preferred.
- Industry experience in financial services, capital markets, investment technology, or other data-intensive environments is preferred.
- Candidates may qualify through equivalent professional experience, demonstrated technical delivery, relevant certifications, or a combination of data engineering, cloud, database, Python, and capital markets experience.
- The role requires willingness to work the 12:00 PM to 9:00 PM shift .

Day-to-Day Activities

- Develop, test, deploy, and support Python-based data solutions and scalable AWS applications involving relational, columnar, and vector databases.
- Collaborate with cross-functional teams to define technical requirements, data models, capital markets use cases, delivery plans, and operational priorities.
- Review database performance, cloud infrastructure, CI/CD processes, application behavior, and production issues while implementing improvements and resolving defects.
- Make technical decisions within approved standards regarding data architecture, schema design, database optimization, AWS deployment, DevOps practices, and solution maintainability.

Qualifications

- Bachelor of Engineering or equivalent qualification in Computer Science, Information Technology, Engineering, or a related field; equivalent relevant professional experience may be considered.
- Minimum of 7–10 years of experience in data engineering, software engineering, cloud engineering, database engineering, DevOps, or a related field.
- AWS certifications are preferred but not required where equivalent practical AWS experience is demonstrated.
- Training or practical experience in Python, AWS, database architecture, data modeling, data warehousing, DevOps, CI/CD, and capital markets is preferred.
- Continuous professional development is expected through relevant certifications, technical training, cloud learning, database education, and knowledge of evolving capital markets technology practices.

Professional Competencies

- Applies structured analysis and problem-solving to investigate data, database, Python, AWS, application, infrastructure, and deployment issues.
- Works effectively with cross-functional teams, contributes technical knowledge, and supports shared delivery goals across data engineering and DevOps activities.
- Communicates technical requirements, database designs, project risks, financial concepts, issues, and recommendations clearly to technical and non-technical stakeholders.
- Adapts to changing AWS services, data architectures, database technologies, capital markets requirements, delivery priorities, and operational needs.
- Identifies practical improvements in data architecture, database performance, automation, deployment reliability, resource usage, and solution sustainability.
- Organizes work across development, testing, troubleshooting, documentation, production support, and delivery commitments while prioritizing tasks according to business impact.

S​ YNECHRON’S DIVERSITY & INCLUSION STATEMENT

Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.

All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law .

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