Data Engineer

Responsibilities

• Design, develop, test, and deploy high-quality, secure, scalable, and resilient data pipelines using Apache Spark, Java/Scala across Hadoop and cloud-native object storage platforms.
• Build and maintain batch and near real-time data processing frameworks capable of supporting petabyte-scale workloads.
• Develop reusable engineering components and frameworks that accelerate data product delivery while maintaining enterprise standards.
• Design and implement a "build once, run anywhere" architecture supporting seamless deployment across on-premises and public cloud environments without code changes.
• Implement data lineage, metadata management, data cataloguing, data quality controls, and observability capabilities across the data ecosystem.
• Collaborate with architects and platform teams to establish scalable design patterns and engineering best practices.
• Contribute to migration initiatives moving legacy ETL and data warehouse workloads from on-premises environments to cloud-native architectures.
• Leverage cloud services such as Amazon S3, EMR, Glue, and related data services to improve scalability, reliability, and operational efficiency.
• Drive adoption of modern Lakehouse and distributed compute architectures.
• Lead end-to-end development activities including requirement analysis, solution design, coding, testing, deployment, and production support.
• Mentor and guide junior engineers through code reviews, technical coaching, and engineering best practices.
• Partner with product owners, analysts, architects, and business stakeholders to deliver high-quality solutions within committed timelines.
• Troubleshoot complex production incidents and perform root cause analysis to identify and implement long-term remediation strategies.
• Ensure compliance with Client's engineering, security, quality assurance, and operational governance standards.
• Continuously identify opportunities to improve performance, automation, monitoring, and process efficiency.
• Evaluate emerging data technologies and conduct proof-of-concept (POC) initiatives to determine their applicability within Client's data ecosystem.
• Contribute to engineering innovation and continuous improvement initiatives across the organisation.

Qualifications

• 8-10 years of experience delivering enterprise-scale Data Warehouse, Data Lake, or Data Lakehouse solutions.
• Proven experience implementing multiple end-to-end data engineering projects within large-scale distributed computing environments.
• Hands-on experience migrating ETL and analytics workloads from on-premises platforms to cloud-native environments.
• Strong development experience using: Apache Spark, Scala or Java, Hadoop ecosystem technologies, Object Storage platforms
• Experience building orchestration and workflow solutions using: Apache Airflow/ Apache NiFi and Similar enterprise scheduling frameworks
• Strong SQL expertise and experience with Relational and NoSQL database technologies: Oracle/ SQL Server, Cassandra, Dynamo DB etc.
• Working knowledge of cloud platforms, preferably AWS: Amazon S3, EMR, AWS Glue, Cloud-native data services
• Strong analytical and problem-solving capabilities.
• Experience operating within Agile delivery environments.
• Excellent written and verbal communication skills.
• Proven ability to collaborate within geographically distributed and matrix-based teams.
• Self-starter with strong ownership, accountability, and execution focus.
• Ability to learn emerging technologies quickly and apply them effectively to business challenges.