Position Overview

We are looking for a skilled and motivated Data Engineer to support the design, development, and optimization of our data infrastructure across multiple cloud environments. The role involves creating reliable data pipelines, developing clean data models, and ensuring data quality and availability for analytics, reporting, and business operations.

The ideal candidate should have at least three years of experience in data engineering or a related field, strong analytical skills, and practical knowledge of modern data tools and frameworks.

Key Responsibilities

• Design, build, and maintain data pipelines to collect, process, and deliver structured and unstructured data.
• Develop and optimize data models to support reporting and analytical needs.
• Work with multiple cloud platforms (AWS, Azure, or GCP) to ensure scalability, performance, and reliability of data systems.
• Support integration of various data sources and APIs into the central data infrastructure.
• Implement data validation, quality checks, and governance processes.
• Collaborate with cross-functional teams to understand business data requirements.
• Assist in documenting workflows, architecture diagrams, and technical specifications.
• Contribute to automation and efficiency improvements across data workflows.

Required Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field.
• Understanding of data modeling, normalization, and database design principles.
• Experience with data integration and transformation workflows.
• Familiarity with at least one major cloud platform (AWS, Azure, or GCP).
• Strong analytical mindset and attention to data accuracy.
• Experience: 3–6 years building production-grade data platforms (ELT/ETL, modeling, reliability).
• Languages: Strong SQL and Python; writing testable, modular code.
• Modeling: Hands-on with dimensional modeling and SCD2; comfort with audit logging and provenance.
• Orchestration & ELT: Real-world experience with a workflow scheduler and transformation framework.
• Warehouses & Storage: Managed Postgres and at least one analytical warehouse (BigQuery or Snowflake); object storage on a major cloud (AWS/GCP/Azure).
• Quality & Testing: Implemented data quality gates, CI for data, and schema enforcement.
• Security: Practical PII handling, access controls, secrets management, and key rotation.
• Reliability: Monitoring, alerting, on-call/readiness mindset; performance tuning of SQL jobs.

Preferred Qualifications

• Experience with data orchestration or workflow tools (e.g., Airflow).
• Knowledge of data warehousing and analytics concepts.
• Exposure to BI or visualization tools (e.g., Power BI, Tableau, Looker).
• Understanding of data governance and security best practices.