General Function The Enterprise Data Analytics team is seeking a hands-on data engineer who can independently design, build, deploy, and support reliable data solutions across a complex enterprise technology environment.

The Engineer - Data Analytics and Engineering will develop the pipelines, integrations, data models, cloud services, automation, and AI-enabled solutions that support enterprise reporting and operations.

This position emphasizes SQL, Python, cloud engineering, data integration, production reliability, and technical problem-solving. The Engineer will own assigned solutions from discovery through deployment and support, working with limited supervision and applying sound technical judgment.

Key Responsibilities

Data Engineering and Integration

- Design, develop, test, deploy, and maintain ETL and ELT pipelines across enterprise applications, APIs, databases, cloud platforms, files, and event-driven services.
- Integrate structured, semi-structured, and unstructured data from ERP, CRM, HCM, ITSM, financial, monitoring, customer, and operational systems.
- Build integrations using REST and GraphQL APIs, database connectors, cloud storage, secure file transfer, and message- or event-based interfaces.
- Design solutions that appropriately handle schema changes, duplicate records, late-arriving data, pagination, rate limits, and partial failures.
- Develop reusable libraries, templates, and configuration-driven processes that reduce one-off development.
- Own assigned pipelines and integrations from technical discovery through production support.

Snowflake, SQL, and Data Modeling

- Design and maintain Snowflake databases, schemas, tables, views, secure views, dynamic tables, streams, tasks, stored procedures, and user-defined functions.
- Develop complex SQL and Snowpark solutions for ingestion, transformation, enrichment, reconciliation, and publication.
- Build scalable dimensional models, analytical marts, curated data products, and semantic-ready datasets.
- Optimize query performance, warehouse utilization, storage patterns, refresh processes, and overall platform cost.
- Select the appropriate balance between SQL transformations, Python processing, AWS services, and external compute.
- Support secure data sharing, downstream applications, embedded analytics, and governed enterprise reporting.



Artificial Intelligence and Emerging Solutions

- Build data pipelines and services that prepare structured and unstructured enterprise data for AI and machine-learning use cases.
- Prototype and productionize AI-assisted workflows using capabilities such as Snowflake Cortex, Amazon Bedrock, document extraction, classification, summarization, embeddings, vector search, and retrieval-augmented generation.

- Integrate AI capabilities into operational workflows while ensuring that outputs are traceable, testable, secure, and grounded in authorized data.
- Develop evaluation methods for AI solutions, including benchmark datasets, accuracy measures, exception handling, human review, and operational monitoring.

- Apply appropriate controls for sensitive data, model inputs, generated outputs, access, and retention.

Quality, Reliability, and Production Support

- Monitor pipeline execution, data freshness, processing volumes, failures, resource consumption, and service-level expectations.

- Troubleshoot production issues across source systems, APIs, networks, pipelines, Snowflake, cloud services, and downstream applications.
- Perform root cause analysis and implement durable corrective actions rather than recurring manual fixes.
- Develop technical documentation, architecture diagrams, operational runbooks, recovery procedures, and incident summaries.

- Identify technical risks, security concerns, scalability limitations, and support requirements before production deployment.
- Contribute to technical standards, reference architectures, naming conventions, and reusable engineering patterns.
- Support enterprise data governance initiatives and contribute to data cataloging and metadata management.

- Identify opportunities to automate manual reporting, streamline data flows, and enhance visibility across systems.

Qualifications
- Bachelor s degree in Computer Science, Software Engineering, Information Systems, Data Engineering, Mathematics,



or a related discipline, or equivalent professional experience.

- Four or more years of experience in data engineering, software engineering, analytics engineering, data integration, or a related field.
- Advanced SQL skills, including complex transformations, procedural logic, performance tuning, and data-model development.
- Strong Python development skills and experience building production scripts, applications, services, or data-processing workflows.

- Hands-on experience designing and supporting ETL or ELT pipelines in an enterprise or cloud environment.
- Experience with Snowflake, AWS, Azure, Databricks, or another modern cloud data platform.
- Professional fluency in written and spoken English, with the ability to independently participate in technical discussions and explain complex concepts clearly.

- Demonstrated ability to design, troubleshoot, and support production solutions with limited day-to-day supervision.
- Strong technical judgment, problem-solving, documentation, and cross-cultural collaboration skills.

Preferred Qualifications

- Advanced experience with Snowflake, including Snowpark, tasks, streams, energetic tables, stored procedures, performance optimization, and account-usage monitoring.

- Experience with AWS serverless, integration, orchestration, monitoring, and security services.
- Experience with Snowflake Cortex, Amazon Bedrock, generative AI, document processing, embeddings, vector search, or retrieval-augmented generation.
- Experience integrating with enterprise platforms such as Oracle Fusion, ServiceNow, Salesforce, Microsoft Graph, or operational-monitoring systems.

- Experience with event-driven architectures, serverless applications, queues, notifications, and distributed workflow orchestration.
- Working knowledge of Amazon QuickSight or another BI platform sufficient to support downstream analytics requirements.
- Experience in a managed services provider, consulting organization, technology services company, or complex multi-customer environment.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Data Analytics and Engineering Professional (Chennai)
🏢 PSRTEK
📍 Chennai