Data Engineer

Design, develop, and maintain scalable ETL/ELT data pipelines to support business and analytics needs

Write, tune, and optimize complex SQL queries for data transformation, aggregation, and analysis

Translate business requirements into well-designed, documented, and reusable data solutions

Partner with analysts, data scientists, and stakeholders to deliver accurate, timely, and trusted datasets

Automate data workflows using orchestration/scheduling tools (Airflow, ADF, Luigi, etc.)

Develop unit tests, integration tests, and validation checks to ensure data accuracy and pipeline reliability

Document pipelines, workflows, and design decisions for knowledge sharing and operational continuity

Apply coding standards, version control practices, and peer code reviews to maintain high-quality deliverables

Proactively troubleshoot, optimize, and monitor pipelines for performance, scalability, and cost efficiency

Support function rollouts, including being available for post-production monitoring and issue resolution

Requirements

Basic Qualifications

Bachelor’s degree in computer science, Information Systems, Engineering, or a related field

2–5 years of hands-on experience in data engineering and building data pipelines

At least 3 years of experience in writing complex SQL queries in a cloud data warehouse/ data lake environment.

Solid hands-on experience with data warehousing concepts and implementations

At least 1 year of experience with Snowflake or another modern cloud data warehouse

At least 1 year of hands-on Python development.

Familiarity on Data modeling and Data warehousing concepts

Experience with orchestration tools (e.g., Airflow, ADF, Luigi)

Familiarity with at least one cloud platform (AWS, Azure, or GCP)

Strong analytical, problem-solving, and communication skills

Ability to work both independently and as part of a collaborative team

Preferred Qualifications

Experience with DBT (Data Build Tool) for data transformations

Exposure to real-time/streaming platforms (Kafka, Spark Streaming, Flink)

Familiarity with CI/CD and version control (Git) in data engineering projects

Exposure to the e-commerce or customer data domain

Understands the technology landscape, up to date on current technology trends and new technology, brings new ideas to the team