symplr

Staff Data engineer

Job description

The objectives of a Staff Data Engineer typically revolve around designing, implementing, and managing robust data architectures and infrastructures. The Staff Data Engineer will play a key role in driving the organization's data strategy, ensuring the efficient flow and storage of data, and supporting data-driven decision-making processes.

Duties & Responsibilities

Duties and Responsibilities:

  • Data Architecture Design:
  • Develop and maintain a scalable and efficient data architecture that meets the organization's current and future needs.
  • Design data models and schemas that support the storage, processing, and retrieval of data.
  • Data Integration:
  • Implement robust ETL (Extract, Transform, Load) processes to integrate data from various sources into a unified and consistent format.
  • Ensure seamless data flow between different systems, databases, and applications.
  • Data Quality and Governance:
  • Establish and enforce data quality standards and governance policies.
  • Implement data validation and cleansing processes to ensure the accuracy and reliability of data.
  • Implement and adhere to data tracking, lineage, and logging strategies for the single pane of glass view for a given data flow.
  • Emerging Technologies:
  • Stay abreast of emerging technologies and trends in the field of data engineering, machine learning (ML) and artificial intelligence (AI) while assessing their applicability to the organization's data engineering needs.
  • Database Management:
  • Manage and optimize database systems, including relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra).
  • Implement database security measures and access controls.
  • Performance Optimization:
  • Identify and address performance bottlenecks in data processing and storage systems.
  • Optimize queries, indexing strategies, and data partitioning for improved performance.
  • Scalability and Reliability:
  • Ensure that data systems are scalable to handle growing volumes of data.
  • Implement redundancy and fault-tolerance measures to enhance data system reliability.
  • Data Security:
  • Implement and monitor security measures to protect sensitive data.
  • Ensure compliance with data privacy regulations and industry standards.

Skills Required

Skills Required

  • Experience with SQL (Preferably Amazon Redshift SQL)
  • Experience with cloud-based data platforms (e.g., AWS)
  • Develop, optimize, and maintain ETL processes for data integration.
  • Solid and current skills in tools like Mulesoft, SSIS, AWS Glue or similar.
  • Familiarity with real-time data processing technologies.
  • Understanding of data modeling concepts and techniques.
  • Design and implement efficient and scalable data models in the cloud.
  • Establish data quality monitoring and validation processes
  • Implement and monitor security measures to protect sensitive data
  • Proficient in writing complex SQL queries.
  • Familiarity with version control systems like Git for managing codebase changes

Qualifications Required

  • Bachelor’s degree (B.A.) from four-year college or university, or equivalent combination of education and experience.
  • 10+ years of experience in data engineering or a related role
  • Proficient in data modeling, ETL development, and database management.
  • Experience with big data technologies and distributed computing frameworks

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