Architect (Elastic Search + Postgres)

 Job Description

Responsibilities:

Design and implement scalable Elasticsearch clusters to meet   business requirements.

Define and maintain index templates, mappings, and settings to   optimize search and analytics use cases.

Develop advanced queries and aggregations for search and reporting   needs.

Implement and manage data lifecycle policies (ILM) for efficient   storage and retrieval.

Optimize Elasticsearch performance through sharding, replication,   and query tuning.

Design solutions for real-time and batch data ingestion using tools   like Logstash, Nifi, beats or custom integrations.

Ensure Elasticsearch security via role-based access control (RBAC),   TLS, and audit trails.

Monitor cluster health and address issues related to performance,   scaling, and fault tolerance.

Design and optimize database schemas for OLTP, OLAP, and hybrid   workloads.

Implement database replication, partitioning, and sharding   strategies for scalability and fault tolerance.

Perform query optimization, indexing strategies, and execution plan   analysis.

Develop and maintain stored procedures, triggers, and custom   functions.

Configure PostgreSQL for high availability using various tools.

Ensure database security, including encryption, user roles, and   permissions.

Implement and maintain backup and recovery strategies using   pg_dump, logical replication, or physical backups.

Troubleshoot and resolve database performance and integrity issues.

Design ETL pipelines for data ingestion and transformation   workflows.

Required Skills

Elasticsearch:

1.   In-depth knowledge of Elasticsearch architecture (nodes, shards,   indices, mappings, and routing).

2.   Hands-on experience with Elastic Stack (Logstash, Kibana, Beats,   APM). Nifi for ETL design.

3.   Strong understanding of search engine concepts, full-text search,   and relevance tuning.

4.   Familiarity with query DSL, aggregations, and custom analyzers.

5.   Proficiency in monitoring tools like Elastic Observability,   Grafana, or Prometheus.

6.   Knowledge of Elasticsearch deployment in cloud environments (AWS,   Azure, GCP).

PostgreSQL:

1.   Deep understanding of PostgreSQL internals, including query   planner, MVCC, and WAL.

2.   Experience with advanced indexing techniques (GIN, GiST, BRIN, and   B-tree).

3.   Knowledge of database normalization and denormalization strategies.

4.   Expertise in PostgreSQL extensions such as PostGIS, TimescaleDB, or   Citus.

5.   Familiarity with tools like pgAdmin, psql, or DBeaver.

6.   Hands-on experience with database migrations and upgrades.

7.   Proficiency in PL/pgSQL scripting.