Data Engineer

Data Engineer

Position Type : Full-Time, Remote Working Hours : U.S. client business hours (with flexibility for pipeline monitoring and data refresh cycles)

About the Role : Our client is seeking a Data Engineer to design, build, and maintain reliable data pipelines and infrastructure that deliver clean, accessible, and actionable data. This role requires strong software engineering fundamentals, experience with modern data stacks, and an eye for quality and scalability. The Data Engineer ensures data flows seamlessly from source systems to warehouses and BI tools, powering decision-making across the business.

Responsibilities :

• Build and maintain ETL / ELT pipelines using Python, SQL, or Scala.
• Orchestrate workflows with Airflow, Prefect, Dagster, or Luigi.
• Ingest structured and unstructured data from APIs, SaaS platforms, relational databases, and streaming sources.
• Manage data warehouses (Snowflake, BigQuery, Redshift).
• Design schemas (star / snowflake) optimized for analytics.
• Implement partitioning, clustering, and query performance tuning.
• Implement validation checks, anomaly detection, and logging for data integrity.
• Enforce naming conventions, lineage tracking, and documentation (dbt, Great Expectations).
• Maintain compliance with GDPR, HIPAA, or industry-specific regulations.
• Develop and monitor streaming pipelines with Kafka, Kinesis, or Pub / Sub.
• Ensure low-latency ingestion for time-sensitive use cases.
• Partner with analysts and data scientists to provide curated, reliable datasets.
• Support BI teams in building dashboards (Tableau, Looker, Power BI).
• Document data models and pipelines for knowledge transfer.
• Containerize data services with Docker and orchestrate in Kubernetes.
• Automate deployments via CI / CD pipelines (GitHub Actions, Jenkins, GitLab CI).
• Manage cloud infrastructure using Terraform or CloudFormation.

What Makes You a Perfect Fit :

Required Experience & Skills (Minimum) :

Ideal Experience & Skills :

What Does a Typical Day Look Like? A Data Engineer's day revolves around keeping pipelines running, improving reliability, and enabling teams with high-quality data. You will :

Key Metrics for Success (KPIs) :

Interview Process :