Top Tech Jobs & Startup Jobs

Develop scalable Java and Spring Boot backend services, REST APIs, microservices, and Spark-based data pipelines. Build Snowflake data solutions, optimize ETL/ELT workflows, and create React or Angular enterprise dashboards. Integrate frontend and backend systems, implement testing, monitoring, CI/CD, and DevOps practices, and troubleshoot production issues. Collaborate with engineering, data, DevOps, and business teams on architecture, code reviews, performance tuning, and root-cause analysis.

Design, develop, and support scalable Java and Apache Spark data-processing applications and AWS ETL/ELT pipelines. Build batch and real-time solutions, optimize Spark performance, manage data ingestion and transformation, implement validation and monitoring, and troubleshoot production issues. Collaborate on architecture, testing, CI/CD, data modeling, code reviews, and root-cause analysis across engineering, DevOps, and business teams.

Administer, secure, monitor, optimize, and troubleshoot enterprise Snowflake environments across development and production. Manage users, roles, access controls, warehouses, performance, costs, data ingestion, integrations, backups, disaster recovery, and compliance. Automate administrative tasks using SQL, Python, shell scripting, and Terraform while supporting CI/CD and infrastructure-as-code deployments. Collaborate with data, security, DevOps, and application teams, resolve production issues, and maintain documentation and operational procedures.

Designs and leads enterprise business intelligence and analytics solutions using Power BI, Microsoft Fabric, Snowflake, Azure Data Factory, and modern data architectures. Responsibilities include developing semantic models, datasets, dashboards, data pipelines, data products, and scalable data platforms; implementing governance, quality, lineage, and metadata practices; enabling self-service analytics; optimizing performance; evaluating emerging tools such as Promethium; and advising stakeholders while mentoring data teams.

Design, build, and support production-grade AWS data pipelines that transform operational data into secure, high-quality, AI-ready datasets. Responsibilities include distributed data processing, Parquet curation, privacy-preserving transformations, orchestration, data quality monitoring, schema management, CI/CD, infrastructure as code, metadata and lineage management, troubleshooting, and reliable backfills.