VP, Process Improvement

Jobtailor

Bengaluru

On-site

INR 4,000,000 - 7,000,000

Full time

11 days ago

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Job summary

Jobtailor is seeking an experienced data engineer to design, build, and operate scalable data pipelines for operational analytics, reporting, and AI-driven automation across cloud and on-premises environments.

You will model, store, and serve large-scale datasets for analytical workloads and AI-enabled systems, integrate data from diverse sources, and ensure pipelines are observable, reliable, and production-ready with governance and metadata practices.

Qualifications

5+ years of hands-on data engineering experience in platform or enterprise environments.
Experience building production-grade data pipelines.
Deep understanding of data lifecycle management, data quality, metadata, and controls in regulated environments.
Experience with business stakeholders in complex operational domains such as fund accounting, middle office, custody, payments, or transfer agency.
Strong SQL skills for data validation and analysis.
Working knowledge of Python or similar for data processing, automation, or integration.
Understanding of ETL/ELT patterns.
Experience with APIs and file-based integrations, including CSV, XML, and vendor feeds.
Understanding of data warehouses, data lakes, and analytical data models.
Understanding of workflow orchestration and scheduling tools.
Experience with data cataloging, data quality tools, and engineering documentation practices.
Experience supporting AI, ML, or generative AI systems through data engineering and governance.
Familiarity with agent-based systems, human-in-the-loop workflows, model/prompt grounding, and decision traceability.
Ability to assess data risks, controls, and guardrails in AI-driven operational workflows.
Passion for hands-on ownership and end-to-end outcomes.
Comfortable operating in a small, high-impact team with significant organizational visibility and influence.

Responsibilities

Design, build, and operate scalable data pipelines for operational analytics and AI-driven automation.
Model, store, and serve large-scale datasets for analytical workloads and low-latency AI and agent-based systems.
Integrate data from vendor feeds, APIs, files, and enterprise platforms.
Ensure pipelines are observable, reliable, production-ready, and operationally rigorous.
Act as Data Steward for assigned business services, owning data quality, lineage, lifecycle management, business definitions, critical data elements, calculation logic, dictionaries, glossaries, and metadata.
Define and enforce data standards, controls, and documentation.
Translate business control requirements into data-level and AI control mechanisms.
Enable AI and intelligent automation with governed inputs for training, inference, and decisioning.
Define agent action constraints, data quality gates, and human-in-the-loop triggers.
Ensure auditability and traceability through decision logs, data lineage, and versioning of rules, prompts, and models.
Establish data quality rules and exception taxonomies.
Monitor data quality dashboards, triage issues, and coordinate remediation.
Align data architecture and integrations with ecosystem dependencies, costs, and execution plans.
Partner with Product Owners to align data definitions and metrics with business outcomes.
Collaborate with engineering, AI, platform, and business teams to prioritize simplification and automation use cases.
Communicate technical concepts to non-technical stakeholders and drive adoption of AI-enabled solutions.
Champion modern data and engineering practices across organizational boundaries.

Skills

Data engineering

SQL proficiency

Python for data processing

ETL/ELT understanding

Data quality

Data integration

Stakeholder collaboration

Problem-solving

Education

Degree in Computer Science

Degree in Engineering

Tools

APIs

Data Quality Tools

Data Warehouses

Data Lakes

Workflow Scheduling Tools

Job description

Design, build, and operate scalable, resilient data pipelines for operational analytics, reporting, and AI-driven automation across cloud and on-premises environments
Model, store, and serve large-scale datasets for analytical workloads and low-latency AI and agent-based systems
Integrate data from vendor feeds, APIs, files, and enterprise platforms
Ensure pipelines are observable, reliable, production-ready, and operationally rigorous
Act as Data Steward for assigned business services, owning data quality, lineage, lifecycle management, business definitions, critical data elements, calculation logic, dictionaries, glossaries, and metadata
Define and enforce data standards, controls, and documentation
Translate business control requirements into data-level and AI control mechanisms
Enable AI and intelligent automation with governed inputs for training, inference, and decisioning
Define agent action constraints, data quality gates, and human-in-the-loop triggers
Ensure auditability and traceability through decision logs, data lineage, and versioning of rules, prompts, and models
Establish data quality rules and exception taxonomies
Monitor data quality dashboards, triage issues, and coordinate remediation
Align data architecture and integrations with ecosystem dependencies, costs, and execution plans
Partner with Product Owners to align data definitions and metrics with business outcomes
Collaborate with engineering, AI, platform, and business teams to prioritize simplification and automation use cases
Communicate technical concepts to non-technical stakeholders and drive adoption of AI-enabled solutions
Champion modern data and engineering practices across organizational boundaries

Requirements

5+ years of hands-on data engineering experience, preferably in platform, infrastructure, or large-scale enterprise environments
Experience building production-grade data pipelines
Deep understanding of data lifecycle management, data quality, metadata, and controls in regulated environments
Experience with business stakeholders in complex operational domains such as fund accounting, middle office, custody, payments, or transfer agency
Strong SQL skills for data validation and analysis
Working knowledge of Python or similar for data processing, automation, or integration
Understanding of ETL/ELT patterns
Experience with APIs and file-based integrations, including CSV, XML, and vendor feeds
Understanding of data warehouses, data lakes, and analytical data models
Understanding of workflow orchestration and scheduling tools
Experience with data cataloging, data quality tools, and engineering documentation practices
Experience supporting AI, ML, or generative AI systems through data engineering and governance
Familiarity with agent-based systems, human-in-the-loop workflows, model/prompt grounding, and decision traceability
Ability to assess data risks, controls, and guardrails in AI-driven operational workflows
Degree in Computer Science, Engineering, or equivalent practical experience in the financial services domain
Passion for hands-on ownership and end-to-end outcomes
Comfortable operating in a small, high-impact team with significant organizational visibility and influence

Core Competencies

Demonstrates expertise in building and managing scalable data pipelines, ensuring data quality and governance in complex operational environments. Proficient in integrating diverse data sources and collaborating with cross-functional teams to drive AI-enabled solutions.

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