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Data Engineer - Remote

IRESH TECHNOLOGIES LLC
Dallas, TX, United States
1 day ago
Apply on www.dice.com
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source
www.dice.com

Tech stack

Airflow Amazon Web Services Microsoft Azure Business Intelligence Development Big Data BigQuery Cloud Computing Computer Programming Continuous Integration Data Architecture Data Validation Data Cleansing
+31 more
Information Engineering Data Governance Data Integration Extract Transform Load (ETL) Data Transformation Data Security Data Warehousing Apache Hadoop Python (Programming Language) Performance Tuning SQL Stored Procedures SQL Databases Unstructured Data Workflow Management Systems Data Processing Freeform SQL Google Cloud Cloud Platform System Snowflake Apache Spark Database Performance Git Data Lakes Pyspark Star Schema Apache Kafka Stream Processing Azure Synapse Analytics Data Pipelines Amazon Redshift Databricks

Job description

We are looking for an experienced Data Engineer with 5-6 years of hands-on experience in designing, developing, and maintaining scalable data pipelines and data processing solutions. The ideal candidate should have strong expertise in SQL, Python, ETL/ELT processes, data warehousing, and cloud-based data platforms. The candidate will be responsible for building reliable data integration solutions, optimizing data workflows, and transforming large volumes of structured and unstructured data into meaningful insights for business intelligence and analytics. Roles & Responsibilities

  1. Design, develop, and maintain scalable ETL/ELT data pipelines to extract, transform, and load data from multiple sources.
  2. Develop complex SQL queries, stored procedures, and optimize database performance.
  3. Build data processing solutions using Python, PySpark, and Spark.
  4. Work with cloud platforms such as AWS, Azure, or Google Cloud Platform (Google Cloud Platform).
  5. Develop and maintain data warehouses, data lakes, and lakehouse architectures.
  6. Implement data integration, data transformation, data cleansing, and data validation processes.
  7. Work with big data technologies such as Apache Spark, Hadoop, Kafka, and Databricks.
  8. Schedule, monitor, and troubleshoot data pipelines using orchestration tools such as Apache Airflow.
  9. Collaborate with Data Analysts, Data Scientists, BI Developers, and business stakeholders.
  10. Ensure data quality, data security, performance optimization, and pipeline reliability.

Requirements

Data Engineering 5-6 years SQL Strong hands-on experience Python Strong programming skills ETL/ELT Hands-on experience Apache Spark / PySpark Good experience Cloud (AWS/Azure/Google Cloud Platform) Experience in at least one Data Warehousing Good understanding Apache Airflow Preferred Databricks Preferred Kafka Good to have Git / CI/CD Working knowledge Preferred Qualifications

  • Experience with Snowflake, Azure Synapse, Amazon Redshift, or BigQuery.
  • Knowledge of Delta Lake and Lakehouse architecture.
  • Experience with dimensional data modeling (Star and Snowflake schemas).
  • Understanding of batch and real-time data processing.
  • Experience implementing data governance and data quality frameworks.
  • Familiarity with Agile/Scrum development methodologies.

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