Data Analytics Developer

Key Responsibilities

• Design, develop, and maintain scalable, high-throughput ETL pipelines to support data integration, transformation, and delivery across enterprise systems.
• Build and optimize data processing workflows using Talend and other ETL technologies for large-scale structured and unstructured datasets.
• Implement data validation, cleansing, deduplication, error handling, logging, and reconciliation mechanisms to ensure data quality and reliability.
• Optimize SQL queries, ETL jobs, and data pipeline performance across relational databases, data lakes, and cloud data warehouses.
• Develop and support real-time and batch data processing solutions, including streaming and event-driven architectures.
• Integrate data from multiple sources, including APIs, databases, cloud platforms, and Kafka-based streaming systems.
• Implement CI/CD processes, version control, containerization, and automated deployment pipelines for data engineering solutions.
• Design and implement monitoring, alerting, and SLA tracking mechanisms to ensure operational stability and performance.
• Support machine learning and advanced analytics initiatives through feature engineering, data preparation, model deployment, and scoring pipelines.
• Collaborate with business, product, analytics, and technology teams to define KPIs, data requirements, and reporting needs.
• Ensure adherence to data governance, security, compliance, encryption, and access management standards.
• Contribute to architecture decisions, process improvements, and best practices for modern data engineering and cloud-native solutions.
  
  

• Strong expertise in ETL development, data integration, and large-scale data pipeline design.
• Hands-on experience with ETL tools such as Talend, Apache NiFi, Airflow, DBT, Hadoop, Spark, or Azure SSIS.
• Proficiency in Java, Python, SQL, and Shell scripting for data processing and automation.
• Strong experience with relational databases and data warehouses including PostgreSQL, Oracle, MySQL, Teradata, Snowflake, and BigQuery.
• Experience with cloud data engineering platforms and services across AWS, Azure, and GCP.
• Strong understanding of data modelling concepts, including star schema, snowflake schema, and normalized data models.
• Experience in performance tuning, query optimization, partitioning, and large-scale data processing.
• Knowledge of API-based integrations, streaming architectures, and Kafka-based event processing.
• Experience implementing CI/CD pipelines using Git, GitHub Actions, Docker, and deployment automation tools.
• Strong understanding of data governance, security controls, IAM, encryption, and data lifecycle management.
• Experience implementing data quality, metadata management, audit, validation, and monitoring frameworks.
• Strong analytical, troubleshooting, problem-solving, and stakeholder management skills.
  
  

• Experience supporting machine learning pipelines, feature engineering, model deployment, and model scoring frameworks.
• Exposure to modern cloud-native architectures and distributed data processing environments.
• Experience working with reporting and visualization platforms such as Power BI or Tableau.
• Familiarity with real-time analytics, event-driven systems, and streaming data platforms.
• Experience collaborating with product, analytics, marketing, and business teams to drive data-driven decision-making.
• Exposure to DevOps practices and infrastructure automation within data engineering ecosystems.
• Experience mentoring junior engineers and contributing to technical leadership initiatives.
• Master’s degree in Computer Science, Information Systems, Data Engineering, or a related discipline.
• Ability to independently lead complex data engineering initiatives and deliver production-ready solutions with minimal supervision.
• Strong communication skills and experience working in cross-functional and geographically distributed teams.