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Metro location, broad technical requirements, and public employer increase competition to medium.
Strong data-tool and cloud requirements moderately limit transferability across industries.
Explicit 7+ years and many mandatory technologies indicate high shortlisting strictness.
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Own design, development, and optimization of scalable, cloud-native data pipelines and automation solutions using SQL, Python, PySpark, Spark, and ETL/ELT frameworks.
Lead large-scale, cross-functional data projects focusing on data quality, infrastructure improvements, and data strategy including KPI definition and measurement frameworks.
Mentor junior analysts, influence data strategy, and translate complex data analyses into actionable business insights for technical and non-technical stakeholders.
7+ years experience in Data Analytics, Data Operations, Data Engineering, Data Science, or related fields in product- or project-driven environments.
Proficient in SQL (Snowflake, BigQuery), Python, PySpark, Apache Spark, Databricks, AWS, GCP, ETL/ELT pipeline development, REST APIs, and Excel.
Hands-on experience with AI/GenAI/LLMs (including Claude Code), AI integrations, MCP protocol, machine learning, NLP, and AI-driven automation solutions.
Experience with web scraping tools (Scrapy, Selenium, BeautifulSoup, Playwright) and building dashboards with Tableau, Looker, or Power BI. Notice period: Not explicitly mentioned in the JD.
Demonstrated ability to lead and mentor teams, provide technical guidance, and successfully deliver cross-functional data initiatives end-to-end.
Skilled in managing multiple concurrent projects in a fast-paced, product-driven environment with high ownership and accountability.
Strong focus on improving data quality at scale with experience leveraging AI/GenAI tools and advanced data engineering practices.