Data engineer

Job Title: Data Engineer                                                                                                                              Location: Chennai / Hyderabad / Pune Work Mode: Work from Office Experience: 4–8 Years Role Description We are seeking a Data Engineer to design, implement, and optimize scalable and high-performance data solutions that support enterprise analytics, AI-driven insights, and digital transformation initiatives. This role will focus on data strategy, architecture, governance, security, and operational efficiency , ensuring seamless data integration across modern cloud platforms. The ideal candidate will collaborate closely with engineering teams, business stakeholders, and leadership to establish a future-ready data ecosystem — balancing performance, cost-efficiency, security, and usability. This position requires expertise in modern cloud-based data architectures , data engineering best practices , and Scaled Agile methodologies.                                                                                                                                           Roles & Responsibilities Design and implement scalable, modular, and future-proof data architectures for enterprise initiatives. Develop enterprise-wide data frameworks that enable governed, secure, and accessible data across various business domains. Define data modeling strategies to support structured and unstructured data, ensuring efficiency, consistency, and usability across analytical platforms. Develop high-performance data pipelines for batch and real-time data processing by integrating APIs, streaming sources, transactional systems, and external data platforms. Optimize query performance, indexing, caching, and storage strategies to enhance scalability, cost efficiency, and analytical capabilities. Establish data interoperability frameworks that enable seamless integration across multiple data sources and platforms. Drive data governance strategies, ensuring security, compliance, access controls, and lineage tracking are embedded into enterprise data solutions. Implement Data Ops best practices , including CI/CD for data pipelines, automated monitoring, and proactive issue resolution to improve operational efficiency. Lead Scaled Agile (SAFe) practices, including Program Increment (PI) Planning, Sprint Planning, and Agile ceremonies, ensuring iterative delivery of enterprise data capabilities. Collaborate with business stakeholders, product teams, and technology leaders to align data architecture strategies with organizational goals. Leverage emerging data technologies and trends to ensure adoption of cutting-edge, scalable, and competitive data solutions Must-Have Skills Proven experience in data architecture, enterprise data management, and cloud-based analytics solutions. Expertise in Databricks, cloud-native data platforms, and distributed computing frameworks. Strong proficiency in data modeling techniques — dimensional modeling, No SQL, and data virtualization. Hands-on experience designing high-performance ETL/ELT pipelines and real-time data processing solutions. Deep understanding of data governance, security, metadata management, and access control frameworks. Experience with CI/CD for data solutions, Data Ops automation, and infrastructure as code (Ia C). Proven collaboration with cross-functional teams, including business executives, data engineers, and analytics teams. Strong problem-solving, strategic thinking, and technical leadership skills. Experience with SQL/No SQL databases, including vector databases for large language models. Experience in data modeling and performance tuning for both OLAP and OLTP databases. Proficiency with Apache Spark, Apache Airflow. Familiarity with software engineering best practices — version control (Git, Subversion), CI/CD (Jenkins, Maven), automated testing, and Dev Ops workflows. Good-to-Have Skills Experience with Data Mesh architectures and federated data governance models. Cloud or enterprise architecture certifications. Knowledge of AI/ML pipeline integration within enterprise data architectures. Familiarity with BI and analytics platforms for enabling self-service analytics and enterprise reporting.