Futurense Technologies
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2. Assemble large, complex data sets that meet business requirements
3. Identify, design, and implement internal process improvements
4. Optimize data delivery and re-design infrastructure for greater scalability
5. Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS technologies
6. Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics
7. Work with internal and external stakeholders to assist with data-related technical issues and support data infrastructure needs
8. Create data tools for analytics and data scientist team members
Skills Required:
1. Working knowledge of ETL on any cloud (Azure / AWS / GCP)
2. Proficient in Python (Programming / Scripting)
3. Good understanding of any of the data warehousing concepts (Snowflake / AWS Redshift / Azure Synapse Analytics / Google Big Query / Hive)
4. In-depth understanding of principles of database structure
5. Good understanding of any of the ETL technologies (Informatica PowerCenter / AWS Glue / Data Factory / SSIS / Spark / Matillion / Talend / Azure)
6. Proficient in SQL (query solving)
7. Knowledge in Change case Management / Version Control – (VSS / DevOps / TFS / GitHub, Bit bucket, CICD Jenkin)
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We don’t just build software—we incubate dedicated engineering teams. From designing systems to shaping team structures and organizational strategy, we enable our clients to launch and scale products that are relevant today and resilient for the future.
Whether you’re scaling an existing product, building from scratch, or optimizing manual processes, we help you move faster with confidence:
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Our approach is rooted in ownership. As a DevOps-driven organization, our engineers take responsibility for the entire lifecycle—from development to release—ensuring quality at every step.
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