Description

Job Description

Responsibilities

• Provide technical input on data platforms, consumption engines and emerging technologies to enhance analytics and derived datasets.
• Design, build and support data pipelines to acquire, aggregate, refine and securely move data from source to target.
• Develop and maintain data assets to enable enterprise querying and analytics.

PostgreSQL / SQL Excellence

• Write production-grade SQL using CTEs, window functions and set-based processing.
• Ensure SQL code is readable, testable, reusable and version-controlled, peer-reviewed via PRs.
• Optimise PostgreSQL performance through:
  • Query tuning and execution plan analysis.
  • Indexing strategies.
  • Partitioning design and maintenance.
  • VACUUM/ANALYZE and table statistics management.
  • Locking and concurrency handling.
• Use EXPLAIN ANALYZE BUFFERS to diagnose slow queries and interpret:
  • Join strategies: nested loop, hash, merge.
  • Sort methods.
  • Buffer hits vs reads, IO hotspots.
• Design effective indexes: B-tree, Hash, GiST, GIN, including composite indexes aligned to filters, joins and sort order.
• Apply index-friendly query patterns:
  • Sargable predicates.
  • Avoiding functions on indexed columns where possible.
  • Minimising implicit casts and datatype mismatches.
• Implement resilient pipelines with proper error handling, retry logic, idempotency and reprocessing/backfill capability.

Data Modelling Standards

• Design and maintain logical and physical data models.
• Apply normalised models (3NF) where appropriate and dimensional models for analytics/data marts.
• Define and maintain data contracts including:
  • Schemas.
  • Keys and constraints.
  • Naming conventions.
  • SCD approach.
  • Canonical business definitions.
• Manage schema evolution safely with backward compatibility, deprecation plans and impact analysis.
• Manage datasets to support feature engineering, APIs and ingestion processes.
• Build, publish and maintain reusable data assets for real-time and analytical consumption.
• Design, implement and document data architecture and data models: conceptual, logical, physical.
• Implement data integration using ETL and other data tools across databases, systems and platforms — good to have.
• Set up data quality checks, monitoring and alerting for analytics data flows.
• Collaborate with data wranglers, data scientists and technical development teams

Key Skills

Education

Any Graduate