AI-Native Data Engineer | Hybrid, Cloud Pipelines & Insights

Palo IT

Ciudad de México

Hybrid

MXN 600,000 - 900,000

Full time

4 days ago

Be an early applicant

A complete application in a minute

Tailored resume and cover letter, ready to send.

Get past ATS filters

Job summary

PALO IT is seeking a Data Engineer in CDMX to design and implement scalable data solutions for an insurance client. You will build resilient data pipelines, enable underwriting analytics, and support fraud prevention with robust data governance.

You will work with Spark, Databricks, dbt, Kafka, and Airflow in a hybrid setup across AWS/GCP/Azure, collaborating with data scientists and business stakeholders. Expect growth opportunities within a global, AI-native consulting environment.

Qualifications

Experience with modern data processing frameworks: Spark, Databricks, dbt, Kafka, Apache Beam.
Strong command of Python and advanced SQL.
Hands-on experience with Azure Data Factory and Synapse.
Knowledge of Data Lake/Data Warehouse design (Snowflake, Redshift, BigQuery).
Familiarity with DataOps, testing, and CI/CD in data pipelines.
Experience with Airflow or Dagster for orchestration.
Understanding data quality and governance frameworks: Great Expectations, Deequ, DataHub, OpenLineage.
Knowledge of streaming technologies: Kafka, Pub/Sub, Kinesis.
Upper-intermediate English (B2+).

Responsibilities

Design, build, and maintain scalable data pipelines in cloud environments.
Integrate large volumes of structured and unstructured data from core systems.
Automate data ingestion, transformation, and quality processes.
Build and manage data lakes and warehouses (Snowflake, BigQuery, Redshift).
Implement data quality, lineage, and governance validations.
Ensure compliance with data regulations and security policies.
Design datasets to support ML, analytics, and dashboards (Power BI, Looker, Tableau).
Collaborate with data scientists, architects, and business stakeholders.
Document data architectures, pipelines, and lineage.