Lead Data Architect
2 days ago
At Seneca Resources, we are seeking a highly skilled Lead Data Engineer to join our team. As a key member of our data engineering team, you will be responsible for designing and implementing data pipelines, overseeing the technical output of a team of data engineers, and ensuring optimal data delivery architecture.
Key Responsibilities:
- Lead a team of data engineers through architecture, design, and implementation of data pipelines.
- Mentor fellow data engineers and contribute to ongoing process improvements for the team.
- Evaluate business needs and objectives and align architecture/designs with business requirements.
- Build data pipelines required for the optimal extraction, transformation, integration, and loading of raw data from various sources.
- Assemble large, complex data sets and model data in a way that meets functional and non-functional business requirements.
- Create data tools for analytics team members to generate innovative industry insights.
- Implement data tagging mechanisms and metadata management to accurately classify and make data visible to the organization.
- Build processes to identify and improve data quality, consistency, and effectiveness.
- Ensure data is managed in accordance with information privacy and protection policies.
- Use agile software development processes to iteratively improve data management systems.
- Identify opportunities for automation and advocate for best practices and continued learning.
Requirements:
- Bachelor's/Tech School degree in Computer Science, MIS, or Engineering, or relevant technical field, and/or commensurate years of real-world experience in software engineering.
- 7+ years of relevant experience in data management.
- 5+ years in data engineering with detailed knowledge of data warehouse technical architectures, infrastructure components, ETL/ELT.
- 3+ years of experience with Cloud-based DW such as Redshift, Snowflake, etc.
- Technical expertise with data models, data mining, and segmentation techniques.
- Experience in data acquisition, transformation, and storage design using design principles, patterns, and best practices.
- Experience with performance analysis and optimization.
- Experience with relational SQL and NoSQL databases, including Postgres and Cassandra.
- Experience with data pipeline and workflow management tools.
- Experience with AWS cloud services: EC2, EMR, RDS, Redshift, Modern data platforms, Snowflake, dbt, Fivetran, and Airflow.
- Experience with stream-processing systems: Kafka, Storm, Spark-Streaming, etc. Data engineering certification is a plus.
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