Wissen Technology is hiring for Senior Data Engineer
About Wissen Technology:
At Wissen Technology, we deliver niche, custom-built products that solve complex business challenges across industries worldwide. Founded in 2015, our core philosophy is built around a strong product engineering mindset—ensuring every solution is architected and delivered right the first time. Today, Wissen Technology has a global footprint with 2000+ employees across offices in the US, UK, UAE, India, and Australia. Our commitment to excellence translates into delivering 2X impact compared to traditional service providers. How do we achieve this Through a combination of deep domain knowledge, cutting-edge technology expertise, and a relentless focus on quality. We have a proven track record of building mission-critical systems across industries, including financial services, healthcare, retail, manufacturing, and more. Our outcome-based projects ensure predictable costs and timelines, while our agile pods provide clients with the flexibility to adapt to their evolving business needs. Our success is powered by top-tier talent. Our mission is clear: to be the partner of choice for building world-class custom products that deliver exceptional impact—the first time, every time.
We are hiring a Senior Data Engineer to design, build, and optimize enterprise scale data platforms using Databricks, Python, and PySpark. This role focuses on developing high-performance batch and real-time data pipelines, implementing modern data engineering frameworks, and delivering reliable, governed data solutions that power analytics, reporting, and AI-driven applications across financial services ecosystems.
Experience : 7 - 12 Years
Mode of Work : Hybrid
Python (7+ years) for developing scalable, modular, and production-ready data engineering applications
PySpark & Apache Spark (5+ years)



including DataFrame API, Spark SQL, Structured
Streaming, performance optimization, partitioning strategies, joins, caching, and handling data skew
Databricks (4+ years) including Delta Lake, Unity Catalog, Databricks Workflows, notebooks, jobs, and end-to-end data engineering capabilities
ETL/Data Engineering/Data Pipeline Development (7+ years) building large-scale batch and real-time data processing solutions
Advanced SQL & Data Modeling including dimensional modeling, slowly changing dimensions (SCD), schema evolution, and query optimization
Apache Airflow (3+ years) for workflow orchestration, DAG development, dependency management, scheduling, monitoring, retries, and backfills
dbt (Data Build Tool) including layered architecture, incremental models, snapshots, macros (Jinja), testing, documentation, and deployment best practices
Cloud Platforms (AWS/Azure/GCP) with hands-on experience building and operating cloud native data solutions.
Microsoft Fabric for data integration, engineering, and analytics workloads
AI-assisted development tools such as GitHub Copilot, Claude Code, or equivalent code generation platforms
CI/CD pipeline implementation using Jenkins, GitHub Actions, Azure DevOps, or GitLab CI
Containerization and orchestration using Docker and Kubernetes
Financial Services, Banking, Capital Markets, or AML domain expertise
Data Quality, Data Observability, and Data Governance frameworks
Education:



BE/BTech/ME/MTech in Computer Science, Information Technology, Engineering, or a related discipline
Experience mentoring junior engineers, conducting code reviews, and contributing to technical design discussions
Strong track record of driving end-to-end delivery of data engineering solutions from architecture through production deployment
Excellent analytical and troubleshooting skills for distributed systems, large-scale data processing, and performance optimization
Quality Focus: Commitment to engineering excellence, coding standards, testing automation, reliability, security, and maintainability
Robust verbal and written communication skills with the ability to collaborate effectively with business, analytics, and engineering stakeholders
Design, develop, and optimize large-scale batch and streaming data pipelines using Python, PySpark, Databricks, and cloud-native technologies
Build and maintain scalable data processing workloads in Databricks with a strong focus on performance, reliability, cost optimization, and maintainability
Develop robust dbt models using layered architecture, incremental processing, snapshots, macros, testing frameworks, and documentation standards
Design and maintain Apache Airflow DAGs for workflow orchestration, operational monitoring, dependency management, retries, and observability
Implement data governance, lineage, access controls, data quality validation, monitoring, and privacy standards across enterprise data platforms
Collaborate closely with analytics teams, product owners, architects, and business stakeholders to transform requirements into high-quality, trusted datasets
Build and enhance CI/CD pipelines to automate testing, deployment, monitoring, and operational readiness of data platforms.
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📌 Senior Data Engineer - AWS, DevOps (Bengaluru)
🏢 wissen technology
📍 Bengaluru