Lead Database Engineer

Job Description :

The core responsibilities for the job include the following :

Database Development :

- Write production-grade SQL for retail microservices complex queries, stored procedures, functions, views, and triggers with a focus on correctness and performance.

- Design and implement database schemas (tables, indexes, partitions) for assigned services under the guidance of the Database Architect.

- Tune slow queries using EXPLAIN ANALYZE; fix index misuse, missing partitions, and vacuum issues in PostgreSQL.

- Implement and test schema migrations safely: backward-compatible changes and zero-downtime deployments.

- Maintain database security: basic RBAC, access controls, and encryption standards as defined by the architect.

ETL and Data Pipelines :

- Support Airflow pipelines that process retail data (1 GB-50 GB daily, ingesting, cleansing, and transforming across DBs/Lakehouse zones.

- Monitor and operate Airflow/Prefect DAGs; troubleshoot failures, fix dependencies, and improve retry logic. Implement data quality checks, null checks, referential integrity, and range validation within pipeline stages.

- Support schema evolution in Delta Lake / Iceberg datasets; handle partition changes and backfills.

Cloud and Tooling :

- Work with managed cloud databases on GCP (Cloud SQL, BigQuery) day-to-day operations and query optimization.

- Set up basic observability: query duration dashboards, replication health alerts, pipeline SLA monitoring in GCP/DataDog.

- Write Python scripts for data automation, validation pipelines, and ad-hoc data investigations.

Collaboration :

- Work with ML/Analytics/Product Engineers to understand data access patterns and support feature extraction queries.

- Participate in sprint planning; estimate DB work accurately and flag risks early.

- Raise design questions to the Database Architect and propose solutions before implementing.

- Document table structures, pipeline logic, and runbooks for systems you build.

Requirements :

- 7-11 years of hands-on database development and data engineering.

- Strong SQL and PostgreSQL stored procedures, triggers, views, indexes, partitioning, query tuning.

- Delta Lake or Apache Iceberg worked in a Lakehouse environment at least as a contributor.

- Cloud database experience: GCP (BigQuery, Cloud SQL).

- Data modeling basics: 3NF, star schema, and understanding why patterns matter.

- Python for scripting, data validation, and automation.

- Airflow or Prefect for pipeline orchestration can operate and debug DAGs.

Good to Have :

- ClickHouse or any columnar OLAP database (Redshift, BigQuery) hands-on queries and table design.

- Retail or e-commerce domain: Familiarity with inventory, pricing, promotions, or demand data.

- NoSQL: Redis, DynamoDB, or MongoDB for specific access patterns.

- GCP Professional Data Engineer certification (studying counts).