Qode
Information Technology and Services
DataEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Data Engineer at Qode. Skills: Data Engineering, Production-grade data pipelines, Scalable data systems, Cloud-based environments, ETL/ELT, CDC, AWS, Python, SQL, dbt, Airflow. 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”
What You'll Achieve.
Building scalable production-grade data pipelines; Building reliable pipelines; Optimizing performance
Industry & Context.
Optimizing performance; Debugging; Backfill/recovery; Query optimization; Debugging incorrect results; Incident handling
What They're Looking For.
Must Have
5+ years of experience in Data Engineering, Proven hands-on ownership of production data pipelines, including design, implementation, deployment, monitoring, debugging, and backfill/recovery, 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 with OLTP databases, preferably MongoDB and PostgreSQL/RDS, Practical experience with schema design, including relational modeling, constraints, indexing, normalization/denormalization, and source-to-target mapping, Experience migrating or syncing data between operational systems and analytical platforms, including validation, cutover, rollback, and reconciliation, SQL skills, including joins, CTEs, window functions, query optimization, and debugging incorrect results, Python or PySpark experience for data processing, automation, validation, and pipeline development, Experience with production pipeline reliability: retries, idempotency, monitoring, alerting, backfill, and incident handling, Hands-on AWS data pipeline experience using services such as S3, Lambda, IAM, RDS, DMS, SQS, Kinesis, Glue, or equivalent
Nice to Have
Advanced dbt modeling, metrics layers, semantic layer, or self-service analytics, BI/dashboarding experience, Great Expectations, Soda, or other data quality frameworks, Databricks Unity Catalog or governance/catalog tools, Terraform or Infrastructure as Code, Docker, Kubernetes, or EKS, CI/CD using GitHub Actions, GitLab CI, or similar, Data warehouse modeling for analytics marts
What You'll Do.
Design and build production-grade data pipelines using batch
and CDC-based patterns
Build ingestion workflows from operational systems such as MongoDB
Design and operate data migration workflows
Convert semi-structured or NoSQL data into reliable relational and analytical models
Build and optimize data processing jobs using Python
Orchestrate workflows using Apache Airflow and manage connectors using Airbyte or similar tools
Maintain data quality
and production reliability across pipelines
Work with AWS services such as S3
testable transformation layers using dbt where appropriate
source-to-target mapping
and operational runbooks
How You'll Work.
Team & Collaboration
Work with systems such as MongoDB, PostgreSQL/RDS, AWS, Airflow, Airbyte, Databricks, dbt, and related data platform tools; Work with AWS services such as S3, Lambda, IAM, EC2, RDS, DMS, SQS, Kinesis, or similar services; Open, collaborative team culture
Communication Scope
Clear communication
Full Job Description
****About the Role**** We are looking for a **Data Engineer** to join our Data Platform team, focusing on building scalable production-grade data pipelines, ingestion systems, and migration workflows across operational and analytical data platforms. In this role, you will work with systems such as **MongoDB, PostgreSQL/RDS, AWS, Airflow, Airbyte, Databricks, dbt** , and related data platform tools. This is a good fit if you enjoy working with large-scale data systems, building reliable pipelines, and optimizing performance in a cloud-based environment. ****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**** * **5+ 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 un
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