Data Engineer

Job Responsibilities

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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 .

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Architect and implement robust data infrastructure capable of handling high-volume data ingestion and processing .

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Develop and manage our central data warehouse in Google BigQuery .

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Design and implement data models, schemas, and table structures optimized for performance, scalability, and long-term maintainability.

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Write clean, efficient, and maintainable SQL and Python code to transform raw data into curated, analysis-ready datasets.

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Build reliable transformation workflows that support analytics, reporting, and data science initiatives .

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Monitor, troubleshoot, and optimize data infrastructure to ensure high performance, reliability, and cost efficiency .

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Implement BigQuery best practices , including partitioning, clustering, query optimization, and materialized views .

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Build and maintain curated data models that serve as the “source of truth” for business intelligence and reporting.

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Ensure data is optimized and readily accessible for BI tools such as Looker and other analytics platforms.

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Implement automated data quality checks, validation rules, and monitoring frameworks to ensure the integrity and reliability of data pipelines and warehouse systems.

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Establish processes for data governance, observability, and lineage tracking .

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Work closely with software engineers, data analysts, and data scientists to understand their data requirements and provide the necessary infrastructure and data products.

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Lead and support client and stakeholder communication , working with enterprise clients to translate business needs into scalable data solutions.

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Partner with product teams and leadership to ensure that technical data solutions align with business strategy and client expectations .

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Take ownership of data platforms and architecture decisions , helping shape the future direction of our analytics and data infrastructure.

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Identify opportunities to improve data reliability, automate workflows, and generate new insights through data .

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Contribute to a collaborative, high-performing engineering culture with strong communication and teamwork.

Basic Qualifications

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

Preferred Qualifications

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