About The Role

The Data Engineer designs and operates the pipelines, data models, and platform services that turn high-volume operational data into reliable datasets for analytics, reporting, and product use cases. The role spans batch and streaming ingestion, transformation, orchestration, data quality, and warehouse performance.

Working with analytics engineers, analysts, software engineers, and product stakeholders, the role ensures data is accurate, discoverable, and available when decisions depend on it. This is a hands-on position for an engineer who cares about production reliability as much as clean schemas and maintainable code.

Key Responsibilities

Build and maintain batch and streaming data pipelines using Python, SQL, and tools such as Apache Spark, Kafka, or equivalent technologies
Design scalable data models and curated datasets in cloud warehouses such as Snowflake, BigQuery, or Redshift
Orchestrate production workflows with Airflow, Dagster, or similar platforms, including retries, dependency management, monitoring, and alerting
Implement data quality checks, lineage, validation, and observability practices that detect freshness, completeness, and schema issues before downstream users are affected
Optimize warehouse queries, pipeline performance, and cloud infrastructure to improve reliability and manage compute costs
Partner with analysts and analytics engineers to define source-of-truth metrics, dimensional models, and well-documented data products
Review code, improve engineering standards, and contribute to architectural decisions around security, governance, testing, and deployment

What We Are Looking For

3–8 years of professional experience in data engineering, analytics engineering, software engineering, or a closely related discipline
Advanced SQL skills and strong Python proficiency, including experience writing tested, maintainable production code
Hands-on experience building ELT or ETL pipelines with modern orchestration and transformation tools such as Airflow, dbt, Spark, or equivalent
Practical experience with at least one cloud data platform, including AWS, GCP, or Azure, and a modern data warehouse such as Snowflake, BigQuery, or Redshift
Strong understanding of data modeling, distributed processing, warehouse architecture, APIs, version control, and CI/CD practices
Bachelor’s degree in computer science, engineering, mathematics, statistics, or a related technical field, or equivalent professional experience
Bonus: Experience with Kafka or other streaming systems, Terraform, Kubernetes, data catalog and lineage tools, privacy-aware data design, or supporting analytics in a regulated environment

Job description