Join a leading IT services company recognized among India's Top 25 Best Workplaces in the IT industry. This role offers a unique opportunity to grow within an award-winning culture that genuinely values talent and ambition, allowing you to own and shape your career success.

Our vision is to be the most trusted technology partner for our clients and a premier destination for top industry professionals. With a global team across India, the U.S.A., and Canada, we empower enterprise clients through technology-enabled transformation in financial services, healthcare, transportation & logistics, and professional services. We specialize in complex, mission-critical applications using cutting-edge technologies like Application & Product Engineering, Data & Analytics, Cloud, DevOps, and MLOps.

Design and implement scalable data engineering solutions using PySpark and advanced distributed data processing frameworks. Architect robust data ingestion, transformation, and processing pipelines aligned with business and analytical goals.

Optimize Snowflake or Delta Lake on Databricks for enterprise-scale data platforms. Lead the development of high-performance batch and streaming data pipelines, and design event-driven architectures using Apache Kafka or Amazon Kinesis.

Establish comprehensive monitoring, scheduling, and operational controls for reliable pipeline execution. Drive data quality, validation, reconciliation, and governance across all data engineering solutions. Implement Lakehouse architecture principles, data observability, and platform engineering standards to enhance scalability and reliability.

Develop business-focused data products by improving data quality, discoverability, and usability. Promote AI-assisted engineering to boost productivity, testing, and documentation. Review designs and implementations to ensure adherence to engineering, scalability, and performance standards. Mentor team members and collaborate with stakeholders for end-to-end data platform delivery.

We are seeking a Senior Engineer with 5-8 years of experience, proficient in Databricks Workflows, Apache Spark, Data Quality & Validation, PySpark, SQL, Apache Kafka, and Snowflake. Experience with Azure Databricks is an added advantage.

Key competencies include strong ownership, effective collaboration with stakeholders, and a quality-focused engineering approach. Possess strong analytical thinking for complex challenges and adaptability in managing evolving technologies.

Effective communication regarding delivery status, risks, and opportunities is essential. Maintain high attention to detail in architecture, design, testing, and implementation. Encourage continuous improvement, knowledge sharing, and mentoring. Balance scalability, performance, reliability, and business priorities while promoting innovation through modern practices and AI-assisted development.