Data Engineer (Remote)

Responsibilities

• Design, build, and maintain production-grade data pipelines (ETL/ELT).
• Develop scalable solutions for processing large datasets.
• Model and structure data for analytics and reporting (fact/dimension models).
• Optimize SQL queries and data storage for performance and cost efficiency.
• Design and implement end-to-end data architectures (batch and near real-time).
• Build and manage data workflows and orchestration pipelines (e.g., scheduling, dependencies, retries).
• Ensure data quality, validation, and reliability across pipelines.
• Implement monitoring, logging, and failure recovery mechanisms.
• Work with cloud platforms to build secure and scalable data solutions.
• Collaborate with analysts, data scientists, and other engineers to support data needs.
• Apply software engineering best practices (version control, CI/CD, testing).
• (Optional) Develop and maintain streaming / real-time data pipelines.

Requirements

• Strong proficiency in Python for data processing and pipeline development.
• Advanced knowledge of SQL (CTEs, window functions, query optimization).
• Experience with data modeling:
  • relational schemas (3NF).
  • dimensional modeling (star/snowflake).
• Hands-on experience with relational databases (e.g., PostgreSQL, MySQL, SQL Server, Oracle).
• Experience with data warehousing platforms (e.g., Snowflake, BigQuery, Redshift, Synapse).
• Understanding of ETL vs ELT, batch vs near real-time processing.
• Experience building and managing data pipelines and orchestration tools (e.g., Airflow, Prefect, Dagster, Azure Data Factory).
• Experience with at least one cloud platform (AWS, Azure, or GCP).
• Knowledge of data quality, validation, and governance practices.
• Familiarity with DevOps practices:
  • Git / version control.
  • CI/CD pipelines.
  • Infrastructure as Code (e.g., Terraform, CloudFormation, ARM).
• Understanding of security best practices (e.g., PII handling, RBAC, encryption).

Nice to have:

• Spark (PySpark/Spark SQL).
• Hadoop basics.
• Streaming (Kafka, Kinesis, Pub/Sub, Event Hubs).
• Java/Scala.
• Bash.
• dbt.
• BI (Power BI, Tableau, Looker).
• APIs/SaaS integration.
• FinOps (cost optimization).
• Data governance/GRC.