Data Engineer

• Collaborate with client teams to understand data sources, formats, and integration requirements. 

• Clean, reconcile, and transform raw customer data to align with our platform’s ingestion standards. 

• Build and maintain data transformation and validation pipelines to ensure consistent, high-quality outputs. 

• Diagnose and resolve data discrepancies, ensuring accuracy and consistency across systems. 

• Develop scripts and workflows for repeatable data processing and format standardization. 

• Partner with internal engineering and client success teams to deliver clean, production-ready datasets. 

• Ability to quickly learn new libraries and APIs to connect to external data sources – for example: Looker SDK, BigQuery python client or Snowflake python connector. 

• Document data mappings, transformation logic, and validation procedures for long-term maintainability. 

• Ability to create effective visualizations to provide insights from the data. 

Qualifications 

• 1–3 years of experience in a data-focused role (data scientist, software engineer, data analysis, data operations, or data engineering). 

• Bachelor’s degree in Computer Science, Information Systems, Statistics, or a related field preferred. 

• Strong proficiency in Python, particularly NumPy and Pandas for data manipulation and transformation. 

• Solid experience with SQL for data querying, joining, and reconciliation across multiple sources. 

• Experience building or maintaining ETL/ELT pipelines or data wrangling workflows. 

• Attention to detail and a commitment to maintaining high data quality and consistency. 

• Ability to interpret and clean messy or incomplete data for use in production systems. 

• Excellent communication skills for collaborating with both technical and non-technical stakeholders. 

Preferred Skills 

• Experience with data validation, schema alignment, or reconciliation across disparate systems. 

• Familiarity with version control, testing, or other software engineering best practices. 

• Exposure to data visualization or reporting tools for validating and communicating results. 

• Background in client-facing or consulting environments where data integrity and delivery were key.