AWS Cloud Data Engineer

Synechron is seeking an AWS Cloud Data Engineer to develop, maintain, and enhance cloud-based data solutions and analytics pipelines. The role focuses on hands-on data engineering, AWS and Databricks development, data integration, and Power BI reporting.

Responsibilities

• Develop, maintain, and support scalable data pipelines using Python, Databricks, and AWS services
• Build data ingestion, transformation, validation, and processing workflows
• Work with structured, semi-structured, and unstructured data sources
• Develop reliable data processing solutions using Databricks and Apache Spark
• Implement data cleansing, enrichment, transformation, aggregation, and validation processes
• Write efficient, maintainable, and well-documented code
• Troubleshoot data quality, pipeline performance, processing, and integration issues
• Collaborate with application developers, data analysts, BI developers, and business stakeholders
• Contribute to data models, data structures, technical specifications, and development documentation
• Develop and support data solutions using AWS cloud services
• Build serverless data-processing components using AWS Lambda
• Use Amazon S3 for data storage, staging, archival, and data-lake-related workflows
• Integrate AWS services into automated data ingestion and processing pipelines
• Implement appropriate error handling, logging, monitoring, and alerting
• Support secure handling of data, credentials, and configuration
• Assist with deployment, troubleshooting, and operational support of AWS-based data workloads
• Follow established AWS development and security standards
• Develop notebooks, jobs, workflows, and data transformations in Databricks
• Use Python and PySpark to process and transform data at scale
• Build and maintain scheduled and event-driven Databricks jobs
• Improve data pipeline performance and optimize processing where necessary
• Implement appropriate partitioning, caching, schema management, and data validation techniques
• Support integration between Databricks, Amazon S3, MongoDB, and other data sources
• Maintain clear documentation for notebooks, jobs, dependencies, and operational procedures
• Develop production-quality Python applications, scripts, utilities, and data-processing components
• Write modular, testable, maintainable, and reusable code
• Implement unit tests, integration tests, validation checks, and error-handling routines
• Use Python libraries and frameworks appropriate to data engineering and AWS development
• Identify and resolve performance, reliability, and maintainability issues
• Follow agreed coding standards, peer-review practices, and development processes
• Work with MongoDB databases, collections, documents, indexes, and queries
• Develop processes to extract data from MongoDB and load it into AWS and Databricks environments
• Support data transformation between document-oriented and analytical data structures
• Write efficient queries and assist with data-performance optimization
• Handle schema variation, nested data, data quality issues, and changing source structures
• Support data reconciliation and validation between source and target systems
• Develop, maintain, and support dashboards, reports, and data models in Microsoft Power BI
• Connect Power BI to appropriate data sources and curated datasets
• Create effective data models, relationships, calculated columns, and measures using DAX
• Develop user-friendly visualizations and dashboards based on business requirements
• Work with business users and analysts to understand reporting needs
• Validate report data and troubleshoot discrepancies between reports and source systems
• Apply appropriate refresh, performance, security, and deployment practices
• Experience with Tableau is highly desirable and may be useful for supporting existing reporting solutions
• Use GitHub for source control, collaboration, pull requests, code reviews, and issue tracking
• Contribute to branching, versioning, and release-management practices
• Develop and maintain GitHub Actions pipelines for automated build, testing, validation, and deployment
• Support automated deployment of Python code, Databricks assets, Lambda functions, and related data components
• Participate in peer reviews and follow established coding and repository standards
• Maintain appropriate documentation for code, pipelines, configuration, and operational procedures
• Troubleshoot failed builds, tests, deployments, and pipeline executions
• Work as an active member of an Agile development team
• Participate in sprint planning, daily stand-ups, backlog refinement, demonstrations, and retrospectives
• Break work into clear technical tasks and provide realistic estimates
• Collaborate with product owners, analysts, developers, testers, and stakeholders
• Deliver incremental, tested, and documented solutions
• Identify risks, dependencies, blockers, and opportunities for improvement
• Contribute to continuous improvement of team development and delivery processes

Skills

• 8+ years of experience in data engineering, cloud development, software development, or a closely related field
• Strong hands-on experience with Databricks and Apache Spark
• Strong Python development experience, particularly for data processing and automation
• Experience with Tableau in addition to Power BI
• Experience with Delta Lake, Unity Catalog, or Databricks Workflows
• Experience working with data lakes, lakehouses, or modern cloud data platforms
• AWS Lambda
• Amazon S3
• IAM
• CloudWatch
• Event-driven or serverless integrations
• Experience working with MongoDB and document-oriented data
• Experience developing and supporting data ingestion and transformation pipelines
• Experience using GitHub for source control and collaborative development
• Familiarity with GitHub Actions and automated CI/CD pipelines
• Strong experience developing reports and dashboards in Microsoft Power BI
• Working knowledge of data modeling, data quality, data validation, and data integration
• Familiarity with Agile software development practices
• Experience writing unit tests, integration tests, and data validation checks
• Strong troubleshooting, analytical, and problem-solving skills
• Ability to work collaboratively within a development team
• Strong written and verbal communication skills

Benefits

• The possibility to work abroad.
• 10 days of paid annual leave (plus sick leave and national holidays).
• Maternity & paternity leave plans.
• A comprehensive insurance plan including medical, dental, vision, life insurance, and long-/short-term disability (plans vary by region).
• Retirement savings plans.
• A higher education certification policy.
• Commuter benefits (varies by region).
• Extensive training opportunities, focused on skills, substantive knowledge, and personal development.
• On-demand Udemy for Business for all Synechron employees with free access to more than 5000 curated courses.
• Coaching opportunities with experienced colleagues from our Financial Innovation Labs (FinLabs) and Center of Excellences (CoE) groups.
• Cutting edge projects at the world’s leading tier-one banks, financial institutions and insurance firms.
• A flat and approachable organization.
• A truly diverse, fun-loving, and global work culture.
• Flexible workplace arrangements.
• Mentoring.
• Internal mobility.
• Learning and development programs.

Company Overview

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