
Data Warehouse Architect
3 days ago
We are seeking a highly skilled data engineer with strong expertise in PostgreSQL and hands-on experience in building scalable data warehouses and lakes. In this role, you will design and implement a data warehouse using PostgreSQL and manage a data lake on AWS infrastructure.
The ideal candidate has a strong foundation in SQL engineering, cloud data architectures, and modern data pipelines. Experience with hosted data warehouse platforms such as DBT, Snowflake, or Databricks is a plus.
Key Responsibilities:- Data Engineering & Architecture
- Design, build, and maintain a scalable data warehouse for analytics and reporting.
- Develop and manage data pipelines for ingestion, transformation, and storage.
- Architect and maintain a data lake on AWS infrastructure.
- Optimize queries, indexing, and schema design for performance and scalability.
- Ensure solutions align with modern open-source data engineering best practices.
- Collaboration & Delivery
- Work closely with product, analytics, and engineering teams to deliver high-quality data solutions.
- Translate business and analytics requirements into scalable data models and pipelines.
- Provide technical expertise to support data-driven decision-making.
- Data Quality & Governance
- Implement data quality checks, lineage tracking, and metadata management.
- Ensure compliance with data security and governance standards.
- Future-Readiness
- Stay current with modern data engineering tools and frameworks.
- Provide guidance on evaluating and integrating new technologies.
- 3+ years of hands-on experience in data engineering or development.
- Expertise in PostgreSQL: advanced SQL, query optimization, schema design, and data modeling.
- Strong experience building ETL/ELT pipelines using Python or open-source frameworks.
- Proficiency with AWS services (S3, Glue, Athena, Redshift Spectrum, Lambda).
- Experience managing structured and unstructured data at scale.
- Solid programming skills in Python.
- Familiarity with open-source data frameworks (Apache Spark, Kafka).
- Opportunity to design and own enterprise-level data platforms.
- A collaborative environment with mentorship from senior leaders.
- Flexibility to innovate and work with open-source-first approaches.
- Growth opportunities in data architecture and leadership.
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