Design, build, and maintain scalable ETL/ELT data pipelines for processing large datasets. Develop and manage data warehouses and data lakes to support business intelligence and analytics. Work with cross-functional teams to understand data requirements.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
Location: United States (100% Remote)
Employment Type: Full-Time / Contract (W2)
Experience Required: 4–8+ Years
Key Responsibilities
- Design, build, and maintain scalable ETL/ELT data pipelines for processing large datasets.
- Develop and manage data warehouses and data lakes to support business intelligence and analytics.
- Write efficient SQL queries and optimize database performance for large-scale data environments.
- Work with cross-functional teams including data scientists, analysts, and product teams to understand data requirements.
- Implement data quality checks, validation frameworks, and monitoring processes.
- Build and maintain cloud-based data infrastructure using platforms such as AWS, Azure, or GCP.
- Optimize data workflows and automate data processing tasks.
- Ensure data governance, security, and compliance standards are followed.
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Required Skills & Qualifications
- Bachelor’s degree in Computer Science, Engineering, or related field.
- 4–8+ years of experience as a Data Engineer or in a similar role.
- Strong experience with SQL and database systems.
- Proficiency in programming languages such as Python, Java, or Scala.
- Hands-on experience with ETL tools and data pipeline frameworks (Airflow, dbt, Informatica, etc.).
- Experience with Big Data technologies such as Spark, Hadoop, or Kafka.
- Strong knowledge of data modeling, data warehousing, and distributed systems.
- Experience with cloud platforms like AWS, Azure, or Google Cloud.
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Preferred Qualifications
- Experience with Snowflake, Redshift, BigQuery, or Databricks.
- Familiarity with containerization and DevOps tools (Docker, CI/CD).
- Experience working in remote or distributed teams.
- Exposure to machine learning data pipelines.
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