Data Engineer
About the role
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