Data Analytics Engineer (Data Platform)

TL;DR

Data Analytics Engineer (Data Platform): Building and maintaining the core data infrastructure for analytics and reporting systems with an accent on ETL pipeline development, PostgreSQL database optimization, and API-ready data services. Focus on standardizing data structures, creating a unified library of analytical logic and KPIs, and ensuring data quality across all ingestion workflows.

Location: Hybrid

Company

hirify.global is seeking a highly experienced Data Platform Engineer to build and maintain the core data infrastructure that powers analytics and reporting systems for its growing technology platform.

What you will do

• Design, build, and maintain automated ETL pipelines from diverse data sources using Python and SQL.
• Architect and optimize PostgreSQL schemas, including indexes, materialized views, and high-performance queries.
• Develop a centralized library of KPIs and analytical rules in Python and SQL for consistent metrics.
• Create reusable object models for key data entities, ensuring standardization and consistent definitions.
• Develop standardized query layers and API-ready data services for internal applications and dynamic reporting tools.

Requirements

• 7+ years of experience in data engineering, ETL development, or data platform roles.
• Expert-level proficiency in Python and advanced proficiency in PostgreSQL, including schema design and SQL optimization.
• Strong background in designing and maintaining ETL pipelines and building analytical metrics frameworks.
• Solid understanding of relational modeling, dimensional modeling, and metadata management.
• Experience developing data services or backend logic consumed by APIs.
• English: B2 required

Nice to have

• Experience with workflow orchestration tools (e.g., Airflow, Prefect, Dagster).
• Background in operational analytics, compliance data, or multi-source data harmonization.
• Familiarity with BI tools such as Tableau or Metabase.

Culture & Benefits

• Opportunity to build and own a complete data foundation for a growing technology platform.
• Direct influence on system architecture, data quality, and analytical capabilities.
• A high-visibility position with meaningful impact on product performance and client deliverables.