Data Engineer

Xpertiz INC

United States

25 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

10 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Application Programming Interfaces (APIs) Agile Methodology Airflow Amazon Web Services Apache HTTP Server Microsoft Azure Cloud Computing Cloud Database Continuous Integration Data Architecture Information Engineering Data Governance

+24 more

Data Integration Extract Transform Load (ETL) Data Warehousing Python (Programming Language) Machine Learning Software Tools DataOps SQL Databases Data Processing Google Cloud Azure Data Factory Snowflake Apache Spark Git Data Lakes Pyspark Google Bigquery Apache Kafka Data Management Video Streaming Data Pipelines Serverless Computing Amazon Redshift Databricks

Job description

We are seeking a highly skilled Senior Data Engineer with 10+ years of experience in designing, developing, and optimizing enterprise-scale data platforms and pipelines. The ideal candidate will have deep expertise in cloud technologies, modern data engineering practices, ETL/ELT development, and data warehousing to support advanced analytics and business intelligence initiatives., * Design, develop, and maintain scalable data pipelines and data integration solutions.

Build robust ETL/ELT processes using modern data engineering tools.
Develop and optimize cloud-based data warehouses and data lakes.
Implement data models to support reporting, analytics, and machine learning workloads.
Optimize SQL queries and improve data processing performance.
Integrate data from multiple enterprise systems using APIs and cloud-native services.
Ensure data quality, governance, security, and compliance standards.
Collaborate with cross-functional teams, including Data Scientists, Analysts, and Architects.
Troubleshoot production issues and optimize existing data workflows.
Mentor junior engineers and promote data engineering best practices.

Requirements

10+ years of experience as a Data Engineer.
Strong expertise in Python, SQL, and PySpark.
Hands-on experience with Apache Spark.
Experience with Snowflake, Databricks, Amazon Redshift, or Google BigQuery.
Strong knowledge of AWS, Azure, or Google Cloud Platform (Google Cloud Platform).
Experience with ETL/ELT tools such as Apache Airflow, dbt, Azure Data Factory (ADF), or Informatica.
Strong understanding of data modeling, data warehousing, and lakehouse architecture.
Experience with Kafka or other streaming technologies.
Proficiency with Git, CI/CD pipelines, and Agile development methodologies., * Experience with Delta Lake, Apache Iceberg, or Apache Hudi.
Knowledge of data governance and security best practices.
AWS, Azure, or Google Cloud Platform Data Engineering certifications are a plus.