Job Description

Role : Senior Database Engineer

Experience: 7 -12 Years

Location: Pune

Mode : Hybrid (3 Days work from Office)

Mandatory Skillset: Database Engineer, Cloud Database, AWS, Postgres, Snowflake, Oracle RDS, Medallion Architecture, DBT, AI/ML Solutions, Data Security, Python, Terraform.

Technical Requirements:

7+ years of experience in data engineering, database engineering, or data platform development in production environments. Strong hands-on experience with Snowflake, including performance tuning, security, and cost optimization. Deep expertise with PostgreSQL and AWS RDS in cloud-native architectures. Proven experience designing lakehouse or modern data warehouse architectures. Strong understanding of Medallion Architecture, semantic layers, and analytics engineering best practices. Experience building and operating advanced ELT pipelines using modern tooling (e.g., dbt, orchestration frameworks). Proficiency with SQL and Python for data transformation, automation, and tooling. Experience with Terraform and infrastructure-as-code for data platforms. Solid understanding of data governance, observability, and reliability engineering.

About the Role :

Join our DevOps Engineering team as a Senior Database Engineer responsible for designing, optimizing, and automating cloud database solutions across AWS RDS, Postgres, and Snowflake. This role focuses on performance engineering, data integration, and automation - ensuring our data platforms are scalable, reliable, and efficient. You'll work closely with DevOps and Product Engineering to build high-performing data infrastructure that supports critical applications and analytics.

Key Responsibilities:

Modern Data Architecture & Platform Engineering:

Design, build, and optimize database solutions using Snowflake, PostgreSQL, and Oracle RDS. Design and evolve cloud-native data lakehouse architectures using Snowflake, AWS, and open data formats where appropriate.

- Implement and manage Medallion Architecture (Bronze / Silver / Gold) patterns to support raw ingestion, curated analytics, and - business-ready datasets.

Build and optimize hybrid data platforms spanning operational databases (PostgreSQL / RDS) and analytical systems (Snowflake). Develop and maintain semantic layers and analytics models to enable consistent, reusable metrics across BI, analytics, and AI use cases. Engineer efficient data models, ETL/ELT pipelines, and query performance tuning for analytical and transactional workloads. Implement replication, partitioning, and data lifecycle management to enhance scalability and resilience. Manage schema evolution, data versioning, and change management in multienvironment deployments.

Advanced Data Pipelines & Orchestration:

Engineer highly reliable ELT pipelines using modern tooling (e.g., DBT, cloud-native services, event-driven ingestion). Design pipelines that support batch, micro-batch, and near-real-time processing. Implement data quality checks, schema enforcement, lineage, and observability across pipelines Optimize performance, cost, and scalability across ingestion, transformation, and consumption layers.

AI-Enabled Data Engineering:

Apply AI and ML techniques to data architecture and operations, including: Intelligent data quality validation and anomaly detection Automated schema drift detection and impact analysis Query optimization and workload pattern analysis Design data foundations that support ML feature stores, training datasets, and inference pipelines. Collaborate with Data Science teams to ensure data platforms are AI-ready, reproducible, and governed.

Automation, DevOps & Infrastructure as Code:

Build and manage data infrastructure as code using Terraform and cloud-native services. Integrate data platforms into CI/CD pipelines, enabling automated testing, deployment, and rollback of data changes. Develop tooling and automation (Python, SQL, APIs) to streamline provisioning, monitoring, and operational workflows.

Security, Governance & Compliance:

Implement enterprise-grade data governance, including role-based access control, encryption, masking, and auditing. Enforce data contracts, ownership, and lifecycle management across the lakehouse. Partner with Security and Compliance teams to ensure audit readiness and regulatory alignment. Build and manage data infrastructure as code using Terraform and cloud-native services. Integrate data platforms into CI/CD pipelines, enabling automated testing, deployment, and rollback of data changes. * Develop tooling and automation (Python, SQL, APIs) to streamline provisioning, monitoring, and operational workflows.