[Urgent] Data Engineer

Location: Remote (Vietnam)
Department: Engineering

About the Position

We are seeking an experienced Data Engineer to join our Data Platform team. You will be responsible for developing and maintaining scalable data infrastructure, ingestion frameworks, and migration solutions that support both operational and analytical systems.

This role is ideal for engineers who enjoy working with large datasets, cloud-based platforms, and building robust, reliable data pipelines in production environments.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines utilizing batch processing, streaming, incremental loading, and Change Data Capture (CDC) methodologies.

• Build and manage data ingestion solutions connecting to sources such as MongoDB, PostgreSQL/RDS, APIs, and event-driven systems.

• Plan and execute data migration projects, including initial loads, incremental synchronization, CDC replay, validation, cutover, rollback, and reconciliation processes.

• Transform semi-structured and NoSQL datasets into well-modeled relational and analytical data structures.

• Develop and optimize data processing workloads using Python, PySpark, Spark SQL, SQL, and Databricks.

• Orchestrate workflows with Apache Airflow and manage data connectors through Airbyte or similar platforms.

• Ensure pipeline reliability through monitoring, alerting, data quality checks, backfill strategies, and operational support.

• Leverage AWS services such as S3, Lambda, IAM, EC2, RDS, DMS, SQS, Kinesis, and related technologies.

• Build reusable and maintainable transformation models using dbt where appropriate.

• Create and maintain technical documentation including data mappings, pipeline architecture, operational procedures, and data contracts.

Qualifications

Required Skills & Experience

• Minimum 6 years of hands-on Data Engineering experience, beyond traditional BI, reporting, or analytics-focused roles.

• Proven track record of owning production data pipelines from architecture and implementation through deployment, monitoring, troubleshooting, and recovery.

• Strong experience designing ETL/ELT solutions with full-load, incremental-load, and CDC architectures.

• Deep understanding of CDC concepts such as idempotency, deduplication, event ordering, delete handling, replay mechanisms, late-arriving data, and reconciliation.

• Practical experience working with operational databases, particularly MongoDB and PostgreSQL/RDS.

• Solid knowledge of database design principles, including indexing, constraints, normalization, denormalization, and source-to-target data mapping.

• Experience migrating and synchronizing data across operational and analytical environments while ensuring accuracy and recoverability.

• Advanced SQL skills, including complex joins, CTEs, window functions, query tuning, and troubleshooting data inconsistencies.

• Strong programming skills in Python and/or PySpark for data engineering and automation tasks.

• Experience building highly reliable pipelines with retry mechanisms, monitoring, alerting, backfill strategies, and incident resolution processes.

• Hands-on experience with AWS-based data platforms and services such as S3, Lambda, IAM, RDS, DMS, SQS, Kinesis, Glue, or equivalent solutions.

Preferred Qualifications

• Experience with advanced dbt implementations, semantic layers, metrics frameworks, or self-service analytics platforms.

• Exposure to BI tools and dashboard development.

• Familiarity with data quality and validation frameworks such as Great Expectations or Soda.

• Experience with Databricks Unity Catalog or other governance and metadata management solutions.

• Knowledge of Infrastructure as Code tools, including Terraform.

• Experience with containerized environments using Docker, Kubernetes, or EKS.

• Familiarity with CI/CD pipelines using GitHub Actions, GitLab CI, or comparable tools.

• Experience designing and modeling data warehouses and analytics marts.