Data Engineer/ Senior

About the Role

Supporting the Director of Engineering and the broader Engineering team, this Senior Data Engineer will operate at both a strategic and hands-on level, working cross-functionally with Product, Design, and Engineering teams.

This is a client-facing position in which you will:

• Partner directly with a specific client
• Help define modern data architecture strategies
• Deliver robust, scalable, and production-ready data solutions

We are looking for someone who can balance architectural vision with hands-on execution.

Responsibilities

Strategic Architecture Leadership

• Define the vision and roadmap for large-scale data architecture across client engagements
• Establish governance, security frameworks, and regulatory compliance standards
• Lead platform selection, integration, and scalability decisions
• Guide organizations in adopting data lakehouse architectures and federated data models

Client Value Creation

• Lead technical discovery sessions to understand client needs
• Translate complex architectures into clear, actionable value for stakeholders
• Build trusted advisor relationships as a strategic technical partner
• Align architecture recommendations with business growth objectives

Technical Architecture & Implementation

• Design and implement modern data lakehouse architectures using Delta Lake and Databricks
• Build and manage scalable ETL/ELT pipelines using Spark (PySpark preferred)
• Leverage Delta Live Tables, Unity Catalog, and schema evolution capabilities
• Optimize storage and queries on cloud object storage (AWS S3, Azure Data Lake, etc.)
• Integrate cloud-native services such as AWS Glue, GCP Dataflow, and Azure Synapse
• Implement data quality monitoring, lineage tracking, and schema versioning
• Build orchestrated pipelines using Airflow, Step Functions, or Cloud Composer

Business Impact & Solution Design

• Develop scalable, compliant, and cost-optimized data solutions
• Design and lead POCs and technical pilots
• Translate business requirements into production-ready data systems
• Define and track success metrics for platform and pipeline initiatives

What We're Looking For

The ideal candidate will have:

• 10+ years of data engineering experience with enterprise-scale systems
• Strong expertise in Apache Spark and Delta Lake (ACID transactions, time travel, Z-ordering, compaction)
• Advanced experience with Databricks (Jobs, Clusters, Workspaces, Delta Live Tables, Unity Catalog)
• Experience building scalable ETL/ELT pipelines (Airflow, Glue, Dataflow, or ADF)
• Advanced SQL for data modeling and transformation
• Strong programming skills in Python (Scala is a plus)
• Experience with data formats such as Parquet, Avro, and JSON
• Experience with schema evolution, versioning, and backfilling strategies
• Experience with at least one major cloud platform:
  • AWS (S3, Athena, Redshift, Glue Catalog, Step Functions)
  • GCP (BigQuery, Cloud Storage, Dataflow, Pub/Sub) – nice to have
  • Azure (Synapse, Data Factory, Azure Databricks) – nice to have
• Experience with real-time or streaming data architectures (Kafka, Kinesis)
• Consulting or client-facing experience with strong communication skills
• Experience with data mesh architectures and domain-driven data design
• Knowledge of metadata management, data cataloging, and lineage tools
• Awareness of international data privacy regulations and compliant system design

Nice to Have

• Experience with healthcare data standards (HL7, FHIR, DICOM)
• Experience with MLOps
• Master's degree in a related field
• Relevant cloud or data engineering certifications

Contract Details

• 4–6 month contract (extension possible)
• 100% remote within LATAM
• 40 hours per week
• Availability during standard business hours as required by the project
• Fluent English (written and spoken) required

Candidates interested in future long-term opportunities will be considered a strong asset.