What are the responsibilities and job description for the IT Data Integration Engineer / AWS Data Engineer ( 3536 ) position at Sharp Decisions?
Job Details
A client of Sharp Decisions Inc. is looking for an IT Data Integration Engineer / AWS Data Engineer to be based in Torrance, CA. This position is HYBRID (3 days a week onsite).The contract duration is 6 months with possible extension. *W2 only.
JOB DESCRIPTION:
The IT Data Integration Engineer / AWS Data Engineer is tasked with the design, development, and management of data integration processes to ensure seamless data flow and accessibility across the organization. This role is pivotal in integrating data from diverse sources, transforming it to meet business requirements, and loading it into target systems such as data warehouses or data lakes. The aim is to support the organization's data-driven decision-making by providing high-quality, consistent, and accessible data.
DAILY TASKS:
Develop and Maintain Data Integration Solutions:
Bachelor's degree in computer science, information technology, or a related field. A master's degree can be advantageous.
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JOB DESCRIPTION:
The IT Data Integration Engineer / AWS Data Engineer is tasked with the design, development, and management of data integration processes to ensure seamless data flow and accessibility across the organization. This role is pivotal in integrating data from diverse sources, transforming it to meet business requirements, and loading it into target systems such as data warehouses or data lakes. The aim is to support the organization's data-driven decision-making by providing high-quality, consistent, and accessible data.
DAILY TASKS:
Develop and Maintain Data Integration Solutions:
- Design and implement data integration workflows using AWS Glue/EMR, Lambda, Redshift
- Demonstrate proficiency in Pyspark, Apache Spark and Python for data processing large datasets
- Ensure data is accurately and efficiently extracted, transformed, and loaded into target systems.
- Ensure Data Quality and Integrity:
- Validate and cleanse data to maintain high data quality.
- Ensure data quality and integrity by implementing monitoring, validation, and error handling mechanisms within data pipelines
- Optimize Data Integration Processes:
- Enhance the performance, optimization of data workflows to meet SLAs, scalability of data integration processes and cost-efficiency on AWS cloud infrastructure.
- Identify and resolve performance bottlenecks, fine-tuning queries, and optimizing data processing to enhance Redshift's performance
- Regularly review and refine integration processes to improve efficiency.
- Support Business Intelligence and Analytics:
- Translate business requirements to technical specifications and coded data pipelines
- Ensure timely availability of integrated data for business intelligence and analytics.
- Collaborate with data analysts and business stakeholders to meet their data requirements.
- Maintain Documentation and Compliance:
- Document all data integration processes, workflows, and technical & system specifications.
- Ensure compliance with data governance policies, industry standards, and regulatory requirements.
Bachelor's degree in computer science, information technology, or a related field. A master's degree can be advantageous.
- 7-10 years of experience in data engineering, database design, ETL processes,
- 5 in programming languages such as PySpark, Python
- 5 years of experience with AWS tools and technologies (S3, EMR, Glue, Athena, RedShift, Postgres, RDS, Lambda, PySpark)
- 3 years of experience of working with databases/ data marts/data warehouses
- Proven experience in ETL development, system integration, and CI/CD implementation.
- Experience in complex database objects to move the changed data across multiple environments
- Solid understanding of data security, privacy, and compliance.
- Excellent problem-solving and communication skills.
- Display good communication skills to effectively collaborate with multi-functional teams
- Participate in agile development processes including sprint planning stand-ups and retrospectives
- Provide technical guidance and mentorship to junior developers
- Attention to detail and a commitment to data quality.
- Continuous learning mindset to keep up with evolving technologies and best practices in data engineering.
#LI-MM1
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