Job Description

Job Description :

Job Title: Data Pipeline delivery + AI Engineer (Sr Analyst)

Experience 5-8 Years

Qualification BE / B. Tech / M. Tech - Computer Engineering

Role & Responsibilities:

Key responsibilities include:

Lead data analysis, validation, cleansing, enrichment, and preparation from multiple sources for analytics, reporting, operational, and AI-enabled use cases.
Design, develop, test, optimize, and monitor AI-assisted ETL/ELT and data ingestion pipelines, with strong emphasis on Snowflake-based enterprise data platforms.
Leverage AI-assisted development techniques to accelerate Python-based data preparation, transformation, orchestration, code generation, troubleshooting, and creation of model-ready datasets for GenAI and analytics.
Develop advanced SQL queries, data profiling, reconciliation, exception handling, and data quality controls to identify anomalies, performance issues, and data improvement opportunities.
Define and maintain technical documentation covering data mappings, transformation rules, lineage, quality controls, runbooks, operational procedures, and production support processes.
Partner with analytics and reporting teams to deliver trusted data products, dashboards, reports, and datasets, while embedding governance, metadata, privacy, security, and auditability standards.
Own and contribute to ETL/ELT testing, automation, performance validation, defect management, release readiness, production monitoring, issue triage, and remediation.
Collaborate with architects, product owners, business stakeholders, and technology teams to translate requirements into scalable solutions, manage dependencies, provide technical guidance, and drive continuous improvement across AI, Snowflake, data engineering, and analytics platforms.

Qualifications

5-8 years of overall IT experience, with 5+ years of recent experience in Data Engineering, Data Warehousing, BI, Analytics Engineering, or enterprise data technology.
Strong hands-on expertise in SQL, data analysis, relational databases, dimensional modeling, and enterprise data warehousing concepts.
Advanced proficiency in Python, with practical experience in PySpark, Shell Scripting, APIs, reusable utilities, and automation frameworks.
Proven experience in designing and implementing ETL/ELT pipelines, data ingestion frameworks, transformation processes, data quality controls, and production-ready data workflows, including AI-assisted development practices.
Hands-on exposure to AI/ML and Generative AI technologies, including prompt-based tools, vector databases, model-ready data preparation, and Retrieval-Augmented Generation (RAG) concepts.
Strong experience with Snowflake and AWS cloud technologies, including developing, optimizing, monitoring, and supporting Python-based data pipelines using AI-assisted development tools.
Strong understanding of data governance, quality, lineage, privacy, security, controls, auditability, and secure data handling within regulated enterprise environments, along with strong analytical and problem-solving skills.
Experience with workflow orchestration, batch processing, scheduling, job monitoring, and production support using tools such as Apache Airflow, AutoSys, or equivalent platforms.