Snowflake & Python Data Engineer – AWS, SQL & Cloud Data Integration

Job Summary Synechron is seeking an experienced Snowflake & Python Data Engineer with 8+ years of overall experience to design, develop, and support scalable data solutions across cloud and on-premises environments. The role will focus on building data integration pipelines, transforming large datasets, automating data workflows, and developing secure data architectures that support enterprise analytics and reporting. The position will contribute to business objectives by improving data availability, quality, reliability, processing efficiency, and accessibility for data engineering, analytics, and business teams.

Software Requirements Required 8+ years of overall professional experience in data engineering, data integration, software development, or related technology roles.

5+ years of experience integrating data to and from cloud and on-premises databases.

3+ years of hands-on experience with Snowflake and Python.

Strong understanding and practical experience with relational databases and SQL.

Hands-on experience integrating data from multiple sources and databases using Python.

Strong hands-on experience with Python programming and shell scripting.

Good knowledge of cloud technologies and services, including AWS S3, EC2, and related services.

Experience designing, developing, testing, and maintaining data pipelines.

Knowledge of data transformation, data validation, data quality, and error-handling practices.

Experience working with Agile development methodologies and distributed teams.

Strong communication, organizational, and analytical skills.

Familiarity with version control, code review, documentation, and software delivery processes.

Ability to support data solutions across both cloud and on-premises environments.

Preferred Knowledge of Python web frameworks such as Django and Flask.

Familiarity with Python ORM libraries.

Knowledge of sub-ledger and ledger processes.

Experience with data warehouse and dimensional modeling concepts.

Familiarity with workflow orchestration, scheduling, monitoring, and alerting tools.

Experience with automated testing and CI/CD practices for data engineering solutions.

Knowledge of cloud security, data governance, metadata management, and access controls.

Experience optimizing Snowflake workloads, SQL queries, storage, and compute usage.

Understanding of sustainable and cost-efficient cloud resource utilization.

Overall Responsibilities Design, develop, and support scalable data pipelines using Snowflake, Python, SQL, and cloud services.

Integrate data from multiple cloud and on-premises databases and source systems.

Develop data transformation, validation, reconciliation, and quality-check processes.

Build and maintain automated data workflows using Python and shell scripting.

Create data models and structures that support analytics, reporting, and operational requirements.

Collaborate with data engineering, analytics, technology, and business teams to understand requirements and deliver appropriate data solutions.

Develop solutions that meet performance, scalability, security, reliability, and maintainability requirements.

Troubleshoot data pipeline failures, data quality issues, performance problems, and integration errors.

Apply Agile development practices within distributed team environments.

Participate in technical design discussions, code reviews, testing, documentation, and release activities.

Identify opportunities to improve data engineering processes, automation, operational efficiency, and data quality.

Support technology decisions related to Snowflake, Python, cloud services, data integration, and data architecture.

Maintain technical documentation for data pipelines, data models, integrations, dependencies, and operational procedures.

Consider data security, cost management, resource efficiency, and sustainability when designing and operating data solutions.

Technical Skills (By Category)

Programming Languages Essential Python using a current and supported version.

Strong understanding of Python programming, including functions, modules, object-oriented programming, exception handling, and testing.

Experience using Python for data integration, data transformation, automation, and workflow development.

Shell scripting for automation, operational support, and data processing.

SQL for data extraction, transformation, validation, analysis, and performance optimization.

Preferred Experience with Python web frameworks such as Django and Flask.

Familiarity with Python ORM libraries.

Experience developing reusable Python packages, utilities, and data-processing components.

Knowledge of scripting practices for cloud and on-premises environments.

Databases/Data Management Essential Strong understanding and practical experience with relational databases.

Strong SQL skills, including joins, subqueries, window functions, common table expressions, data manipulation, and query optimization.

At least 3+ years of experience working with Snowflake.

Experience integrating data from multiple databases and source systems.

Knowledge of data modeling, data structures, data transformation, and data quality.

Understanding of data reconciliation, validation, error handling, and exception management.

Experience working with both cloud and on-premises databases.

Preferred Experience with dimensional modeling, fact and dimension tables, and analytical data warehouse design.

Knowledge of Snowflake performance tuning, warehouse sizing, partitioning, clustering, and workload management.

Familiarity with metadata management, data lineage, data governance, and master data concepts.

Knowledge of sub-ledger and ledger processes.

Experience with NoSQL databases or specialized data stores.

Cloud Technologies Essential Good knowledge of AWS technologies and services, including S3 and EC2.

Understanding of cloud storage, compute, networking, access management, and monitoring concepts.

Experience integrating cloud-based services with on-premises databases and applications.

Understanding of cloud-based data processing, deployment, availability, and operational support.

Preferred Experience implementing Snowflake solutions within cloud environments.

Familiarity with cloud-native data integration and processing services.

Knowledge of cloud security, identity and access management, encryption, secrets management, and network controls.

Experience with infrastructure automation and cloud monitoring.

Understanding of cost optimization and sustainable use of cloud resources.

Frameworks and Libraries Essential Experience using Python libraries for database connectivity, data processing, file handling, validation, and automation.

Knowledge of libraries used to connect Python applications with relational databases and Snowflake.

Ability to select, integrate, maintain, and upgrade relevant libraries.

Preferred Knowledge of Django and Flask.

Familiarity with Python ORM libraries.

Experience with data processing libraries and reusable transformation frameworks.

Knowledge of workflow orchestration and scheduling frameworks.

Familiarity with testing frameworks for Python and data pipelines.

Development Tools and Methodologies Essential Proven experience working with Agile development methodologies.

Experience working with distributed technology teams.

Familiarity with version control, branching, merging, pull requests, and code reviews.

Knowledge of project tracking, documentation, collaboration, and delivery tools.

Experience with software development lifecycle practices, including requirements analysis, design, development, testing, deployment, and maintenance.

Ability to document data flows, pipeline dependencies, data models, operational procedures, and technical decisions.

Preferred Experience with CI/CD pipelines for Python and data engineering solutions.

Familiarity with automated data pipeline testing and quality validation.

Knowledge of workflow scheduling, monitoring, logging, alerting, and incident management.

Experience with infrastructure as code and automated deployment practices.

Familiarity with performance monitoring and operational dashboards.

Security Protocols Essential Understanding of secure data handling and data protection principles.

Awareness of authentication, authorization, identity and access management, encryption, secrets management, and secure connectivity.

Ability to apply appropriate access controls to data pipelines, databases, cloud services, and Snowflake environments.

Understanding of data privacy, data integrity, and secure transfer practices.

Preferred Experience implementing security controls across cloud and on-premises data environments.

Knowledge of data masking, tokenization, role-based access control, and audit logging.

Familiarity with vulnerability management, security testing, and compliance-related data controls.

Understanding of secure configuration and monitoring practices for data platforms.

Experience Requirements 8+ years of overall experience in data engineering, data integration, software development, or related technology roles.

5+ years of experience integrating data to and from cloud and on-premises databases.

3+ years of hands-on experience with Snowflake and Python.

Strong experience with relational databases and SQL.

Hands-on experience integrating data from multiple sources and databases using Python.

Strong experience with Python programming and shell scripting.

Experience working with cloud technologies and services, including S3, EC2, and related services.

Experience designing, developing, testing, troubleshooting, and supporting data pipelines.

Proven experience working with Agile development methodologies and distributed teams.

Experience collaborating with data engineering, analytics, technology, and business stakeholders.

Experience with Snowflake data warehousing, data transformation, data modeling, and performance optimization is preferred.

Knowledge of sub-ledger and ledger processes is preferred.

Experience in financial services, banking, insurance, accounting, reporting, or other data-intensive industries is preferred.

Candidates may also qualify through equivalent practical experience, relevant technical training, certifications, or demonstrated project outcomes that provide the required capabilities.

Day-to-Day Activities Develop, maintain, and monitor Snowflake and Python data pipelines that integrate cloud and on-premises data sources.

Collaborate with distributed Agile teams, data engineers, analysts, business stakeholders, and technology partners to clarify requirements and delivery priorities.

Create data transformations, validations, reconciliation processes, documentation, testing outputs, and production support deliverables.

Investigate pipeline and data quality issues, recommend technical solutions, and make implementation decisions within established architecture, security, and delivery standards.

Qualifications Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or a related field; equivalent professional experience may be considered.

Minimum of 8+ years of overall professional experience, including 5+ years in cloud and on-premises data integration and 3+ years with Snowflake and Python.

Certifications in Snowflake, cloud technologies, data engineering, Python, database management, or Agile delivery are preferred.

Complete Synechron-required training related to information security, data protection, data governance, workplace conduct, and responsible technology use.

Maintain continuous professional development in Snowflake, Python, SQL, cloud data platforms, data architecture, security, automation, and Agile delivery.

Professional Competencies Critical thinking, analytical reasoning, data investigation, and structured problem-solving.

Effective collaboration with distributed, cross-functional, data, technology, and business teams.

Clear communication, stakeholder engagement, documentation, and knowledge sharing.

Adaptability, continuous learning, and application of evolving data engineering technologies.

Innovation focused on scalable, secure, reliable, automated, cost-effective, and sustainable data solutions.

Effective organization, time management, prioritization, and delivery of multiple data engineering activities.

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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