Qode
Information Technology and Services
DataEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Data Engineer at Qode. Skills: Data Pipelines, ETL/ELT, AWS, Python. Design and build data pipelines. Build ingestion workflows”
Industry & Context.
debugging; incident handling; query optimization; debugging incorrect results
What They're Looking For.
Must Have
6+ years of experience in Data Engineering, Proven hands-on ownership of production data pipelines, experience building ETL/ELT pipelines, Good understanding of CDC correctness, Hands-on experience with OLTP databases, Practical experience with schema design, Experience migrating or syncing data, SQL skills, Python or PySpark experience, Experience with production pipeline reliability, Hands-on AWS data pipeline experience
Nice to Have
Advanced dbt modeling, metrics layers, semantic layer, self-service analytics, BI/dashboarding experience, Great Expectations, Soda, data quality frameworks, Databricks Unity Catalog, governance/catalog tools, Terraform, Infrastructure as Code, Docker, Kubernetes, EKS, CI/CD using GitHub Actions, GitLab CI, Data warehouse modeling for analytics marts
What You'll Do.
Design and build data pipelines
Build ingestion workflows
Design and operate data migration workflows
Convert semi-structured or NoSQL data
Build and optimize data processing jobs
Orchestrate workflows using Airflow
Maintain data quality
Work with AWS services
testable transformation layers
How You'll Work.
Team & Collaboration
work with systems such as MongoDB; work with AWS services; Open, collaborative team culture; async teamwork
Communication Scope
communication
Full Job Description
**Data Engineer** **Location:** Vietnam **Workplace Type:** Remote **About the Role** Our client is looking for a Data Engineer to join their Data Platform team, focusing on building scalable data pipelines and enabling analytics across the organization. In this role, you will work with systems such as MongoDB, PostgreSQL/RDS, AWS, Airflow, Airbyte, Databricks, dbt, and related data platform tools. **Responsibilities** * Design and build production-grade data pipelines using batch, streaming, incremental, and CDC-based patterns. * Build ingestion workflows from operational systems such as MongoDB, PostgreSQL, RDS, APIs, and event streams. * Design and operate data migration workflows, including full load, incremental sync, CDC replay, cutover, rollback, and reconciliation. * Convert semi-structured or NoSQL data into reliable relational and analytical models. * Build and optimize data processing jobs using Python, PySpark, Spark SQL, SQL, and Databricks. * Orchestrate workflows using Apache Airflow and manage connectors using Airbyte or similar tools. * Maintain data quality, observability, alerting, backfills, and production reliability across pipelines. * Work with AWS services such as S3, Lambda, IAM, EC2, RDS, DMS, SQS, Kinesis, or similar services. * Build modular, testable transformation layers using dbt where appropriate. * Document data flows, source-to-target mapping, pipeline behavior, data contracts, and operational runbooks. **Requirements** * 6+ years of experience in Data Engineering, not only analytics, BI, or reporting. * Proven hands-on ownership of production data pipelines, including design, implementation, deployment, monitoring, debugging, and backfill/recovery. * Strong experience building ETL/ELT pipelines with full load, incremental load, and CDC-based patterns. * Good understanding of CDC correctness, including idempotency, deduplication, ordering, deletes/tombstones, late-arriving events, replay, and reconciliation. * Hands-on experience wi
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