Senior Database Engineer - Platform Engineering
IntegriChain5 months ago
Pune, IndiaSenior
Responsibilities
- Design and build hybrid cloud data platforms across PostgreSQL, Aurora, RDS, Azure SQL, Redshift, Snowflake, DynamoDB, DocumentDB, and OpenSearch.
- Architect AWS lakehouse platforms using S3, Lake Formation, Glue, Apache Iceberg, Delta Lake, and Parquet.
- Engineer ELT/ETL, batch, micro-batch, streaming, orchestration, CDC, data quality, lineage, and observability workflows.
- Develop DynamoDB single-table schemas, DocumentDB clusters, OpenSearch/Elasticsearch clusters, caching layers, replication topologies, partitioning, and lifecycle automation.
- Build automated schema migration, data versioning, snapshot/restore, environment cloning, and CI/CD workflows.
- Develop internal platform tooling, APIs, SDKs, and self-service database provisioning capabilities using Python, SQL, and AWS SDK.
- Implement infrastructure as code with Terraform and AWS CDK across databases, networking, IAM, and supporting data services.
- Apply AI and ML techniques to data quality, anomaly detection, schema drift, query analysis, vector search, and MLOps-ready data foundations.
- Implement governance and security controls including RBAC, row- and column-level security, encryption, masking, audit logging, IAM, private endpoints, and automated secrets rotation.
- Partner with engineering, product, security, compliance, and data science teams to improve platform performance, reliability, scalability, and governance.
Requirements
- At least 7 years of experience in database platform engineering, data engineering, or cloud infrastructure engineering in production environments.
- Lead or senior-level experience designing multi-engine platforms across SQL and NoSQL workloads with a software engineering mindset.
- Deep production experience with AWS RDS, Aurora, DynamoDB, Snowflake, and infrastructure managed as code.
- Strong SQL, data modeling, Medallion Architecture, semantic layer, analytics engineering, and NoSQL modeling expertise.
- Experience building ELT/ETL and streaming pipelines with dbt, AWS Glue, Airflow, Kinesis, MSK/Kafka, or comparable tools.
- Experience with CDC patterns and tools such as AWS DMS or Debezium.
- Understanding of feature engineering, training dataset preparation, model versioning, inference data patterns, and ML data infrastructure.
- Exposure to AWS SageMaker, Amazon Bedrock, vector databases, pgvector, or OpenSearch k-NN is preferred.
- Proficiency with Terraform, Python, boto3, SQLAlchemy, pandas, and SQL; AWS CDK experience is a plus.
- Experience integrating database workflows into CI/CD using GitHub Actions, AWS CodePipeline, or similar tools.
- Familiarity with Azure SQL, Azure Data Factory, Azure Synapse, Snowflake governance, open table formats, MLOps tools, observability platforms, graph databases, time-series databases, or Databricks is advantageous.
- AWS or Snowflake certifications are nice to have.
Benefits
- Medical benefits and non-medical perks are offered.
- Student loan reimbursement is available.
- Flexible paid time off and paid parental leave are provided.
- 401(k) plan with company match is offered.
- Employees receive learning and development opportunities, including more than 700 free development courses.
Tech Stack
Amazon DynamoDBAmazon RedshiftApache AirflowApache KafkaAWSDatabricksdbtElasticsearchFlywayGitHub ActionsMLflowMongoDBMySQLPandasPostgreSQLPythonRedisSnowflakeSQLTerraform
Categories
Data EngineeringDevOps