Data Engineer

Overview

On Site
Hybrid
Depends on Experience
Contract - W2
Contract - 12 Month(s)

Skills

Amazon Web Services
PySpark
GraphQL
Jenkins
Kubernetes
Snow Flake Schema
Python
Apache Spark
Apache Kafka
Data Engineering
Data Governance
Data Processing
Data Modeling
Data Validation
Databricks
Terraform

Job Details

W2 candidates are preferred.

Requirements:

  • Strong programming experience in Python and SQL for data transformation and automation.
  • Expertise in ETL / ELT pipeline design, development, and optimization.
  • Hands-on experience with Snowflake (data modeling, performance tuning, and query optimization).
  • Strong understanding of AWS data services:
  • S3, Glue, Lambda, Athena, Redshift, Step Functions.
  • Experience with Databricks for data processing, notebooks, and Delta Lake implementation.
  • Proficiency with Apache Spark (PySpark / SparkSQL) for distributed data processing.
  • Experience building data ingestion frameworks using tools like Kafka, Kinesis, or Fivetran.
  • Solid understanding of data lakehouse architectures and best practices for structured/unstructured data.
  • Experience with workflow orchestration tools Airflow, AWS Step Functions, or Prefect.
  • Knowledge of data warehousing concepts, star/snowflake schema, and data modeling techniques (Dimensional, 3NF).
  • Familiar with CI/CD pipelines for data engineering using Git, Jenkins, or GitHub Actions.
  • Strong understanding of data governance, metadata management, and data quality frameworks.
  • Experience working with containerization (Docker, Kubernetes) for scalable data deployments.
  • Exposure to modern data stack tools like DBT, Terraform, Great Expectations (for data validation).
  • Familiarity with REST APIs or GraphQL for integrating external data sources.
  • Experience with performance tuning, query optimization, and cost management in cloud environments.
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