Sr. Database Administrator

Position Summary:

We are seeking a visionary Enterprise Data Architect to lead the modernization, design, and governance of our global data ecosystem. This role focuses on transitioning legacy data structures into modern, scalable architectures while establishing rigorous data governance and functional isolation. The ideal candidate possesses deep expertise in polyglot persistence, database decomposition, and PostgreSQL internals, and is passionate about enterprise modernization—specifically removing technical debt by decoupling heavy procedural SQL and shifting business logic into stateless microservices and application layers.

Key Responsibilities:

● Database Decomposition & Architecture: Architect the transition of large, monolithic databases into functionally isolated, bounded-context databases to seamlessly support domain-driven design and microservices architectures.

● Legacy Modernization: Lead initiatives to extract, transform, and remove legacy procedural SQL (e.g., stored procedures, triggers) from the database tier into modern application and service layers, utilizing automated transformation and refactoring toolsets.

● Data Governance & Quality: Establish robust data governance frameworks to assign clear data ownership, eliminate data duplication, and ensure a single source of truth across the enterprise data ecosystem.

● Data Platform Strategy: Define and execute the enterprise data roadmap, ensuring that database and data lakehouse designs align with scalability, security, and long-term business objectives across multi-cloud environments (AWS, GCP, Azure).

● Cross-Functional Leadership: Collaborate with software engineering, product, and DevOps teams to ensure database solutions are highly available, seamlessly integrated, and optimized for both reads and writes.

Technical Expertise & Core Skills

● Database & Polyglot Persistence:

○ Ability to design modern database architectures tailored to specific use cases, combining relational (SQL), NoSQL (e.g., MongoDB, DynamoDB), and In-Memory databases (e.g., Redis).

○ PostgreSQL Mastery: Must possess advanced, hands-on knowledge of PostgreSQL. This includes a proven ability to navigate the nuances of database configuration, performance tuning, vacuuming strategies, connection pooling, spatial data handling (PostGIS), and replication.

● Data Warehouse & Data Lakehouse:

○ Deep understanding of Online Transaction Processing (OLTP), Online Analytical Processing (OLAP), and modern data warehousing paradigms.

○ Expertise in designing and implementing modern Data Lakehouse architectures using platforms such as Snowflake, Databricks, Google BigQuery, or Amazon Redshift.

● Data Formats & Open Table Standards:

○ Proficiency with open table formats that enable transactional reliability on data lakes, specifically Apache Iceberg, Delta Lake, and Apache Hudi.

○ Strong knowledge of columnar and row-based file formats for efficient data ingestion and storage, including Apache Parquet, ORC, Avro, and JSON.

● Modern ETL / ELT & Orchestration:

○ Hands-on experience with modern ELT transformation tools, most notably dbt (data build tool).

○ Familiarity with automated data ingestion and movement platforms such as Fivetran, Airbyte, or dlt.

○ Experience with data orchestration and pipeline management frameworks like Apache Airflow, Dagster, Prefect, or AWS Glue.

Required Experience

● 10+ years of experience in database architecture, data engineering, or enterprise data management.

● Proven track record of breaking down complex monolithic databases into smaller, decoupled databases tailored for macroservices or microservices.

● Extensive experience leading data governance programs, data cataloging, and master data management (MDM).

● Demonstrated success in modernizing legacy database architectures and migrating heavy database-side business logic to application services. Success Measures

● Successful decoupling of legacy monolithic databases into domain-specific data stores.

● Measurable reduction in database-tier business logic and procedural SQL.

● Establishment of a clear data governance model with strictly defined data ownership and minimized data redundancy.

● Deployment of a unified data lakehouse architecture utilizing modern open table formats (e.g., Iceberg) and ELT pipelines.