Data Engineer

Job Responsibilities

• Design, build, and maintain scalable and reliable batch and real-time ETL/ELT data pipelines using cloud services such as GCP Dataflow, Cloud Functions, Pub/Sub, and Cloud Composer.
• Architect and implement robust data infrastructure capable of handling high-volume data ingestion and processing.
• Develop and manage our central data warehouse in Google BigQuery.
• Design and implement data models, schemas, and table structures optimized for performance, scalability, and long-term maintainability.
• Write clean, efficient, and maintainable SQL and Python code to transform raw data into curated, analysis-ready datasets.
• Build reliable transformation workflows that support analytics, reporting, and data science initiatives.
• Monitor, troubleshoot, and optimize data infrastructure to ensure high performance, reliability, and cost efficiency.
• Implement BigQuery best practices, including partitioning, clustering, query optimization, and materialized views.
• Build and maintain curated data models that serve as the “source of truth” for business intelligence and reporting.
• Ensure data is optimized and readily accessible for BI tools such as Looker and other analytics platforms.
• Implement automated data quality checks, validation rules, and monitoring frameworks to ensure the integrity and reliability of data pipelines and warehouse systems.
• Establish processes for data governance, observability, and lineage tracking.
• Work closely with software engineers, data analysts, and data scientists to understand their data requirements and provide the necessary infrastructure and data products.
• Lead and support client and stakeholder communication, working with enterprise clients to translate business needs into scalable data solutions.
• Partner with product teams and leadership to ensure that technical data solutions align with business strategy and client expectations.
• Take ownership of data platforms and architecture decisions, helping shape the future direction of our analytics and data infrastructure.
• Identify opportunities to improve data reliability, automate workflows, and generate new insights through data.
• Contribute to a collaborative, high-performing engineering culture with strong communication and teamwork.

Basic Qualifications

• 5+ years of hands-on experience in data engineering, data integration, or data platform development.
• Degree in Computer Science, Engineering, Mathematics, or related STEM discipline.
• Strong programming and query skills in SQL and Python.
• Experience working with distributed version control systems such as Git in an Agile/Scrum environment.
• Experience designing and orchestrating ETL pipelines, particularly with Databricks.
• Experience working within cloud environments (GCP, AWS, or Azure).
• Experience with database systems such as MongoDB and Elasticsearch.
• Strong understanding of data warehousing and dimensional modeling methodologies.
• Hands-on experience with Airflow and Hadoop.
• Experience using Docker for containerized workflows and reproducible environments.
• Ability to identify opportunities to improve data quality, reliability, and automation.
• Strong business awareness and communication skills, with the ability to collaborate with both technical teams and business stakeholders.
• Experience within the retail industry is a plus.

Preferred Qualifications

• Master’s degree in Computer Science, Engineering, or related discipline.
• Experience working with enterprise-scale data platforms and Fortune 500 clients.
• Familiarity with Druid and its Python API, including Kafka integrations.
• Strong experience using Apache Spark for large-scale data processing.
• Experience designing real-time streaming data architectures.
• Experience working with AI-driven platforms, data infrastructure supporting AI/ML systems, or agentic AI workflows