60 Elt Jobs Hiring Near You

Duties & Responsibilities
• Design, develop, and maintain scalable data pipelines and ETL processes to support data integration and analytics.
• Collaborate with data architects, modelers and IT team members to help define and evolve the overall cloud-based data architecture strategy, including data warehousing, data lakes, streaming analytics, and data governance frameworks
• Collaborate with data scientists, analysts, and other business stakeholders to understand data requirements and deliver solutions.
• Optimize and manage data storage solutions (e.g., S3, Snowflake, Redshift) ensuring data quality, integrity, security, and accessibility.
• Implement data quality and validation processes to ensure data accuracy and reliability.
• Develop and maintain documentation for data processes, architecture, and workflows.
• Monitor and troubleshoot data pipeline performance and resolve issues promptly.
• Consulting and Analysis: Meet regularly with defined clients and stakeholders to understand and analyze their processes and needs. Determine requirements to present possible solutions or improvements.
• Technology Evaluation: Stay updated with the latest industry trends and technologies to continuously improve data engineering practices.
Requirements
Data Engineer
• Associate degree in Computer Science or MIS with a minimum of 4 years experience; or Bachelor degree in Computer Science, or MIS, or related field with a minimum of 2 years of experience; or a Master degree in Computer Science, MIS, with minimum 1 year of experience; or relevant Business or IT experience of minimum of 4 years.
• Moderate level of technical understanding and demonstrated knowledge in integrating data between applications and data warehouses
• Knowledge of project management and experience working on project teams required.
• Moderate knowledge of and experience with life cycle methodology,
• Exposure to or experience in Expertise in one or more of a major cloud platform (AWS, Azure or GCP)
• Moderate knowledge of and experience with the software development lifecycle.
• Experience working in project teams and contributing to a successful outcome
• Hands-on experience with AWS services such as AWS Glue, Lambda, Athena, Step Functions, and Lake Formation
• Proficiency in Python and SQL
Sr. Data Engineer • Associate degree in Computer Science, MIS or related field with a minimum of 8 years experience; or Bachelor degree in Computer Science, or MIS, or related field with minimum of 4 years of experience; or a Master degree in Computer Science, MIS, or related field with minimum 2 years of experience; or relevant Business or IT experience of minimum of 4 years.
• Cloud Expertise: Expert-level proficiency in at least one major cloud platform (AWS, Azure, or GCP) with extensive experience in their respective data services (e.g., AWS S3, Glue, Lambda, Redshift, Kinesis; Azure Data Lake, Data Factory, Synapse, Event Hubs; GCP BigQuery, Dataflow, Pub/Sub, Cloud Storage); experience with AWS data cloud platform preferred
• SQL Mastery: Advanced SQL writing and optimization skills.
• Data Warehousing: Deep understanding of data warehousing concepts, Kimball methodology, and various data modeling techniques (dimensional, star/snowflake schemas).
• Big Data Technologies: Experience with big data processing frameworks (e.g., Spark, Hadoop, Flink) is a plus.
• Database Systems: Experience with relational and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB, Cassandra).
• DevOps/CI/CD: Familiarity with DevOps principles and CI/CD pipelines for data solutions.
• Hands-on experience with AWS services such as AWS Glue, Lambda, Athena, Step Functions, and Lake Formation
• Proficiency in Python and SQL
• Desired Skills, Experience and Abilities
• 4+ years of progressive experience in data engineering, with a significant portion dedicated to cloud-based data platforms.
• ETL/ELT Tools: Hands-on experience with ETL/ELT tools and orchestrators (e.g., Apache Airflow, Azure Data Factory, AWS Glue, dbt).
• Data Governance: Understanding of data governance, data quality, and metadata management principles.
• AWS Experience: Ability to evaluate AWS cloud applications, make architecture recommendations; AWS solutions architect certification (Associate or Professional) is a plus
• Familiarity with Snowflake
• Knowledge of dbt (data build tool)
• Strong problem-solving skills, especially in data pipeline troubleshooting and optimization