Data Engineer

As a Data Engineer, you’ll design, build, and operate scalable, reliable data pipelines and data infrastructure. Your work will ensure high-quality data is accessible, trusted, and ready for analytics and data science - powering business insights and decision-making across the company.

What you’ll do:

• Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations
• Develop and evolve scalable data architecture to meet business and performance requirements
• Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics
• Implement best practices for data quality, testing, monitoring, lineage, and reliability
• Optimize workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimization, partitioning strategies)
• Ensure secure data handling and compliance with relevant data protection standards and internal policies
• Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes
• Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices
• Build and maintain MLOps automation: CI/CD for ML, environment management, artifact handling, versioning of data/models/code

This role is perfect for you if you have: 

• Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience)
• 3+ years of experience as a Data Engineer, building and maintaining production-grade pipelines and datasets
• Python and SQL skills with a solid understanding of data structures, performance, and optimization strategies for ETL/ELT processes
• Hands-on experience with orchestration (like Airflow, Dagster, Databricks Workflows) and distributed processing in a cloud environment
• Familiarity with at least one major cloud provider (GCP, AWS, Azure) and deploying data solutions in the cloud
• Strong troubleshooting mindset: ability to debug issues across data, infra, pipelines, and deployments
• Collaborative mindset and clear communication across engineering, analytics, and business stakeholders

Nice to have:

• Strong GCP experience and ecosystem knowledge: BigQuery, Composer, Dataproc, Cloud Run, Dataplex, Cloud Storage
• Experience building reliable incremental data ingestion pipelines from DBs and APIs
• Experience with analytical data modeling (star and snowflake schemas), DWH, ETL/ELT patterns, and dimensional concepts
• Familiarity with CI/CD for data pipelines, IaC (Terraform), and/or DataOps practices
• Experience with data governance concepts: access control, retention, data classification, auditability, and compliance standards
• Experience building observability for data systems (metrics, alerting, data quality checks, incident response)
• Knowledge of model monitoring concepts: drift, data quality issues, performance degradation, bias checks, and alerting strategies