Data Engineer

Experience

• 3–6 years of hands-on experience in data engineering, analytics engineering, or a related technical role.
  
• Demonstrated experience building or maintaining data pipelines in a professional setting.
  
• Exposure to cloud-based data platforms, preferably Azure (Databricks, Data Factory, or Synapse).
  

Technical Skills — Required

dbt:

• Working knowledge of dbt model development including staging and mart layers.
  
• Familiarity with dbt tests, documentation, and source configurations.
  
• Eagerness to deepen dbt skills including incremental models and CI/CD integration.
  

Databricks:

• Hands-on experience with Databricks notebooks and basic job/workflow setup.
  
• Familiarity with Delta Lake concepts and Databricks SQL.
  
• Exposure to PySpark for data transformation tasks.
  

SQL:

• Solid SQL skills: joins, CTEs, window functions, aggregations, and basic performance awareness.
  
• Experience writing SQL for data transformation and validation in a cloud data warehouse.
  

Pipeline Engineering:

• Experience building or supporting ELT pipelines with monitoring and basic data validation.
  
• Familiarity with pipeline orchestration tools such as Azure Data Factory or Databricks Workflows.
  

Python:

• Basic to intermediate Python skills for data processing, scripting, and automation.
  
• Familiarity with PySpark is a plus.
  

Data Modeling:

• Understanding of star/snowflake schemas and fact & dimension table concepts.
  
• Exposure to Lakehouse or medallion architecture (Bronze/Silver/Gold) is a plus.
  

Soft Skills

• Curious and eager to learn with a proactive approach to problem-solving.
  
• Good communication skills — able to collaborate across technical and non-technical teams.
  
• Attention to detail and a strong sense of data quality.
  
• Comfortable working in a collaborative, fast-paced, and remote team environment.
  

Preferred Additional Requirements

• Experience with Databricks or Azure Synapse Analytics.
  
• Familiarity with D365 CRM or Similar data structures.
  
• Exposure to Git-based workflows and CI/CD practices for data pipeline deployments.
  
• Experience in a humanitarian, nonprofit, or international development context.
  

Pipeline Engineering & Orchestration

• Build and maintain ELT data pipelines using Databricks Workflows and Azure Data Factory for batch and scheduled processing from internal and external sources.
  
• Support the ingestion of data from key systems (e.g., D365 CRM, ServiceNow) into Lakehouse.
  
• Monitor pipeline execution, identify failures, and troubleshooting issues in collaboration with senior engineers.
  
• Contribute to pipeline documentation and help maintain runbooks and process standards.
  

dbt Development

• Develop and maintain dbt models across staging, intermediate, and mart layers under the guidance of senior team members.
  
• Write dbt tests and contribute to source freshness checks to support data quality.
  
• Learn and apply dbt best practices including modular design, ref dependencies, and incremental model patterns.
  
• Work with analysts and business teams to translate data requirements into dbt models.
  

SQL & Data Transformation

• Write intermediate to advanced SQL for data extraction, transformation, and validation tasks.
  
• Apply SQL techniques including joins, CTEs, window functions, and aggregations to support reporting and analytics needs.
  
• Assist in query optimization and performance troubleshooting within
  

Databricks SQL environments.

• Support data model maintenance and help accommodate new source fields or schema changes.
  

Databricks & Cloud Platform

• Develop and maintain Databricks notebooks and jobs for data transformation workloads.
  
• Gain hands-on experience with Delta Lake concepts and PySpark for data processing.
  
• Follow Lakehouse design patterns (Bronze/Silver/Gold) as defined by the Data Architect.
  
• Support cloud resource management including basic cluster configuration and job scheduling.
  

Collaboration & Learning

• Actively collaborate with the Data Team on pipeline design, troubleshooting, and delivery.
  
• Participate in code reviews and incorporate feedback to improve code quality.
  
• Support documentation of processes, standards, and data flows
  
• Engage with Finance, FP&A, and other business teams to understand data needs and assist in solution delivery.