qode

qode

qode is a company that focuses on unlocking global opportunities and unleashing potential through no-code solutions. They provide tools and services to help individuals and businesses develop software without the need for traditional coding skills.

Internet Software & Services

Description

  • 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, and Kinesis.
  • 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 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.
  • Strong SQL skills, including joins, CTEs, window functions, query optimization, and debugging incorrect results.
  • Strong Python or PySpark experience for data processing, automation, validation, and pipeline development.
  • Experience with production pipeline reliability, including 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.
  • Advanced dbt modeling, metrics layers, semantic layer, or self-service analytics experience is preferred.
  • BI/dashboarding experience is preferred.
  • Experience with Great Expectations, Soda, or other data quality frameworks is preferred.
  • Experience with Databricks Unity Catalog or other governance/catalog tools is preferred.
  • Experience with Terraform or Infrastructure as Code is preferred.
  • Experience with Docker, Kubernetes, or EKS is preferred.
  • Experience with CI/CD using GitHub Actions, GitLab CI, or similar tools is preferred.
  • Experience with data warehouse modeling for analytics marts is preferred.

Benefits

  • Attractive salary range, with room to negotiate for strong candidates.
  • Hybrid/remote-friendly culture with flexibility to work where you perform best.
  • Flexible hours and async teamwork that respects focus time.
  • Work equipment support.
  • Allowance for certification and skill development.
  • Year-end bonus and performance-based rewards.
  • 15 paid leave days per year.
  • Career growth support with personal coaching sessions.
  • Open, collaborative team culture with no micromanagement and high trust.
  • Tools and AI-powered workflows that make remote work easier.

Interested in this position?

Apply directly on the company website

Apply Now

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