Are you a PostgreSQL expert who thrives on optimizing complex queries, managing massive retail data pipelines, and architecting robust Lakehouse environments Taglynk is looking for a seasoned Lead Database & Data Engineer to join our team!
If you have a passion for zero-downtime deployments, high-performance SQL, and scalable data automation, we want to hear from you.
Role Overview
- Position: Lead Database / Data Engineer
- Experience: 7–11 years of hands-on experience
- Core Tech: PostgreSQL, Airflow, GCP, Delta Lake / Iceberg
Key Responsibilities
Database Development
- Production-Grade SQL: Write complex queries, stored procedures, functions, views, and triggers for high-velocity microservices with a strong focus on correctness and performance.
- Schema Design: Design and implement database schemas (tables, indexes, partitions) under the guidance of our Database Architect.
- Performance Tuning: Tune slow queries using EXPLAIN ANALYZE, fixing index misuse, missing partitions, and vacuum issues in PostgreSQL.
- Migrations: Safely implement and test backward-compatible schema migrations for zero-downtime deployments.
- Security: Maintain database security using basic RBAC, access controls, and encryption standards.
ETL & Data Pipelines
- Pipeline Support: Support Airflow pipelines processing retail data (1GB–50GB daily), managing ingestion, cleansing, and transformation across DBs and Lakehouse zones.
- DAG Management: Monitor and operate Airflow/Prefect DAGs, troubleshoot failures, fix dependencies, and improve retry logic.
- Data Quality: Implement strict data quality checks (null checks, referential integrity, range validation) within pipeline stages.
- Schema Evolution: Support schema evolution in Delta Lake / Apache Iceberg datasets, handling partition changes and backfills.
Cloud, Tooling & Automation
- Cloud Infrastructure: Work with managed cloud databases on GCP (Cloud SQL, BigQuery) for day-to-day operations and query optimization.
- Observability: Set up query duration dashboards, replication health alerts, and pipeline SLA monitoring via GCP/DataDog.
- Scripting: Write Python scripts for data automation, validation pipelines, and ad-hoc data investigations.
Collaboration & Documentation
- Partner with ML, Analytics, and Product Engineers to understand data access patterns and support feature extraction queries.
- Participate in sprint planning, estimate database work accurately, and flag technical risks early.
- Propose solutions and raise design questions to the Database Architect before implementation.
- Document table structures, pipeline logic, and system runbooks.
Must-Have Qualifications
- 7–11 years of hands-on database development and data engineering experience.
- Strong SQL & PostgreSQL expertise: Deep experience with stored procedures, triggers, views, indexes, partitioning, and query tuning.
- Lakehouse Ecosystems: Proven contributor experience in a Lakehouse environment using Delta Lake or Apache Iceberg.
