Senior Data Engineer - Data Platform
J
JobgetherData engineering
Fully remote position in Mexico.Full-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Native-level Spanish and professional English
- Experience
- 5+ years of experience in data engineering, including at least 2 years operating as a Senior Data Engineer or technical platform reference.
- Required Skills
- AWSPostgreSQLPythonSQLAirflowCI/CDdbtRedshift
Requirements
- Have 5+ years of data engineering experience, including at least 2 years as a Senior Data Engineer or technical platform reference.
- Bring strong Python experience for production pipelines, ETL/ELT, automation, and data services.
- Have experience applying Git, pull requests, code reviews, testing, and CI/CD to data engineering.
- Have hands-on experience with Dagster or Airflow; deploying or migrating an orchestration platform from the ground up is preferred.
- Have experience with managed ingestion tools such as Airbyte or Fivetran, and custom API connectors.
- Understand incremental processing, idempotency, and historical data management.
- Have advanced SQL skills and experience with PostgreSQL and/or analytical warehouses such as Redshift, Snowflake, or BigQuery.
- Have experience with dbt or an equivalent transformation framework and understand the relationship between ingestion and data modeling.
- Have cloud experience, preferably AWS services including RDS, Redshift, Lambda, and S3.
- Have experience implementing data quality checks, monitoring, and alerting using Datadog, Grafana, or equivalent.
- Demonstrate ability to modernize legacy or manual processes into scalable, cloud-native infrastructure.
- Communicate effectively in native-level Spanish and professional English.
Responsibilities
- Design and operate managed and custom data ingestion using Airbyte, APIs, and CDC for clinical, commercial, and operational sources.
- Build incremental, idempotent pipelines with appropriate historical data handling.
- Own Dagster and Python orchestration workflows, including dependencies, retries, backfills, SLAs, and assets.
- Build production-grade batch and near-real-time pipelines, including event-driven architectures where appropriate.
- Establish data quality checks, monitoring, alerts, and lineage across data layers.
- Evolve cloud architecture using AWS services such as PostgreSQL/RDS, Lambda, S3, Redshift, and serverless technologies.
- Maintain engineering practices across the data platform, including Git, pull requests, testing, CI/CD, templates, and reusable components.
- Enable Analytics Engineering, Data Science, and ML teams with reliable raw data, training datasets, reusable features, and prediction pipelines.
- Collaborate with product and business stakeholders to translate needs into data solutions and communicate technical trade-offs.
- Review code, pair with colleagues, mentor engineers, and establish scalable technical standards.
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