Job Description

Locations

• Hyderabad, Telangana, India

Salary

Skills Required

• hands-on technical workintermediate
• designing thoughtful scalable servicesintermediate
• building data architecturesintermediate
• optimizing ETL workflowsintermediate
• developing tools and frameworksintermediate
• ensuring data quality and integrityintermediate
• collaborating with cross-functional teamsintermediate
• instrumenting APIsintermediate
• instrumenting user journey and interaction flowsintermediate
• collecting behavioral dataintermediate
• collecting transactional dataintermediate
• collecting operational dataintermediate
• enabling robust analyticsintermediate
• enabling insightful reportingintermediate
• integrating data pipelines into MLOpsintermediate
• automating data flow for feature engineeringintermediate
• automating model retrainingintermediate
• performance monitoring models in productionintermediate
• drift detectionintermediate
• ensuring scalabilityintermediate
• constructing data pipelines for Gen AIintermediate
• constructing data pipelines for RAG solutionsintermediate
• data extractionintermediate
• data chunkingintermediate
• data embeddingintermediate
• data groundingintermediate
• continuous quality measurementintermediate
• continuous performance measurementintermediate
• AI engineeringintermediate
• Big Data engineeringintermediate
• ML enablementintermediate
• generative AI initiativesintermediate
• data-driven decision-makingintermediate

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science or a related technical field or equivalent experience (experience)
• 4+ years of experience in designing, developing and deploying data engineering for analytics or ML & AI pipelines. (experience, 4 years)
• Strong proficiency in SQL, Scala, Python, or Java, with hands-on experience in data pipeline tools (e.g., Apache Spark, Kafka, Airflow), CI/CD practices, and version control. (experience)
• Familiarity with cloud platforms (AWS, Azure, GCP) and data management and analytics tools like Snowflake, Databricks and Tableau. (experience)
• Strong understanding of data warehousing, data modeling (dimensional/star schemas), and metric standardization. (experience)
• Strong problem-solving skills and the ability to work in an agile environment. (experience)

Preferred Qualifications

• Ability to create technical Specs, instrumentation specs and posses the ability to understand APIs, MSDs etc.. (experience)
• Expertise in building and refining large-scale data pipelines, as well as developing tools and frameworks for data platforms. (experience)
• Hands-on experience with big data technologies such as distributed querying(Trino), real-time analytics(OLAP), near-real-time data processing (NRT), and decentralized data architecture (Apache Mesh). (experience)
• Familiarity with data governance, security protocols, and compliance in financial data systems. (experience)
• Experience enabling ML pipelines including automating the data flow for feature engineering, model retraining, performance monitoring models in production, drift detection and ensuring scalability. (experience)
• Familiarity with GenAI concepts like Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), prompt engineering, vector embeddings, and LLM fine-tuning (experience)
• Works independently with minimal oversight, actively builds relationships, and contributes to a positive team environment. (experience)
• Demonstrates sound judgment, applies technical principles to complex projects, evaluates solutions, and proposes new ideas and process improvements. (experience)
• Seeks new opportunities for growth, demonstrates a thorough understanding of technical concepts, exercises independence in problem-solving, and delivers impactful results at the team level. (experience)
• Familiarity with Fintech, Wallet domain, digital commerce etc.. (experience)

Responsibilities

• - Instrument APIs, user journey and interaction flows to systematically collect behavioral, transactional, and operational data, enabling robust analytics and insightful reporting
• - Build robust data architectures for Wallet, Payments & Commerce products.
• - Build robust data architectures supporting large-scale Wallet Payments & Commerce (WPC) applications.
• - Optimize ETL workflows to enhance data processing efficiency and reliability.
• - Develop tools and frameworks to optimize data processing performance.
• - Ensure data quality and integrity across all data systems and platforms.
• - Collaborate closely with diverse set of partners to gather requirements, prioritize use cases, and ensure high-quality data products delivery.
• - Integrate data pipelines into the broader ML Operations (MLOps) process. This includes automating the data flow for feature engineering, model retraining, performance monitoring models in production, drift detection and ensuring scalability.
• - Construct and maintain data pipelines for Gen AI/RAG solutions, including processes for data extraction, chunking, embedding, and grounding to prepare data for models and perform continuous quality and performance measurement.

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