Principal Data Engineer

Invok Hr; Pune District; Hybrid; INR 600,000 - 900,000; Full time

Job summary

Invok Hr in Pune is seeking a Principal Data Engineer to lead the Finance Data Hub and define six foundational engineering frameworks for data pipelines and platforms. The role emphasizes platform engineering, governance, and adoption of GenAI tools to accelerate data product delivery.

The ideal candidate has deep Snowflake expertise, strong SQL/Python skills, and experience building scalable data pipelines, data quality, and observability frameworks in a DataOps environment.

Qualifications

• BE/M.Tech in Electrical/Electronics/CS or equivalent.
• 12+ years of experience in data engineering or platform engineering at scale.
• Proven ability to lead cross-functional engineering teams.

Responsibilities

• Define and codify six engineering frameworks for the Finance Data Hub data engineering operating model.
• Lead DataOps and CI/CD pipelines for data workloads with automated testing and deployment.
• Implement data quality, observability, and governance frameworks across the data lifecycle.
• Architect Snowflake platform patterns: RBAC, RLS, masking, and access control at scale.
• Champion GenAI-augmented data engineering and tooling adoption with guardrails.
• Drive modernization initiatives to improve cycle time and data product delivery.

Skills

Platform engineering

DataOps

CI/CD pipelines

Data governance

GenAI tooling

Education

BE/M.Tech in Electrical/Electronics/CS

Tools

Snowflake

GitHub Actions

Python

SQL

Job description

What youll do:

If you desire to be part of something special, to be part of a winning team, to be part of a fun team winning is fun. We are looking forward to hire Principal Data Engineer in Pune,

India. This exciting role offers opportunity to:

• The Senior Data Engineer is a pivotal role within the Finance Data Hub and the Enterprise Data platform, focused on establishing standards, building frameworks, and elevating engineering capabilities across data organization.
• Rather than solely delivering features, this position defines and codifies principles and practices for building, testing, deploying, monitoring, and governing data pipelines in a modern DataOps and data mesh environment.
• The impact extends beyond functional pipelines, creating a reusable foundation that empowers every data team member to deliver high-quality data products efficiently and confidently.
• The ideal candidate brings deep technical expertise, a platform engineering mindset, and strong leadership to drive adoption of new standards, with a forwardlooking approach to GenAI-augmented data engineering.
• This role is directly accountable for establishing, documenting, and driving adoption of six foundational engineering frameworks that will define the data engineering operating model for the Finance Data Hub:
• This role will define and codify leading practices across the full data lifecycle including
• DataOps framework for CI/CD-driven pipeline deployment, automated unit testing,
• Data Quality framework with data contract testing, schema validation, and anomaly detection,
• Data Observability standard for end-to-end lineage tracking, freshness monitoring, and incident response,
• Data Modeling standard aligned to medallion or dimensional patterns with naming conventions and style guides,
• Data Governance and Access Control framework covering classification, masking, and role-based access, and
• Pipeline Design Pattern library of reusable, idempotent, and testable ELT/ETL templates.

Qualifications:

Requirement :

• B E/M.Tech in Electrical/Electronics/Computer Science
• 12+ years
• End-to-end delivery of production data pipelines at enterprise scale: ingestion,

transformation, orchestration, and serving layers. Strong SQL and Python

proficiency

• Experience with both batch and streaming paradigms
• Technical leadership in a cross-functional environment setting standards,

mentoring engineers, conducting design reviews, and influencing engineering

direction without necessarily holding a direct management title

• Deep hands-on Snowflake expertise: data sharing, zero-copy cloning, dynamic

tables, streams and tasks, RBAC design, row access policies, dynamic masking,

warehouse sizing, and query optimization. Snowflake certification is a strong plus

• Proficient with GitHub for version control, pull request workflows, and GitHub

Actions for CI/CD automation. Experience designing branching strategies and

automated test/deploy pipelines for data workloads

• Hands-on experience building transformation tools models, tests, macros,

packages, sources, and exposures. Coalesce experience or familiarity is an

advantage. Understanding of DAG-based transformation orchestration

• Has designed and implemented RLS frameworks at the platform layer (e.g.,

Snowflake row access policies). Understands the intersection of data governance

policy and platform enforcement

• Has implemented data quality monitoring frameworks and observability

instrumentation in production environments

• Strong grasp of medallion architecture (Bronze/Silver/Gold), dimensional modeling

(star schema, SCD types), and modern lakehouse/warehouse modeling patterns.

Has published or enforced modeling standards

• Has led or meaningfully contributed to a data engineering modernization initiative

re-platforming, cycle time reduction, or adoption of modern tooling. Can articulate

before/after outcomes with metrics

• Has experimented with or productionised GenAI tools to enhance data engineering

workflows AI code assistants, LLM-powered documentation, natural language

querying, or AI-driven anomaly analysis.

• Has built or adopted reusable automated unit testing frameworks for data pipelines

or transformation models. Understands test pyramid concepts in a data context:

unit, integration, and contract tests

Skills:

1. Framework Authorship & Adoption Leadership

Design, document, and version-control all six engineering frameworks in a central

standards repository (GitHub), ensuring they are discoverable, living documents with clear

change governance.

Conduct framework enablement sessions, workshops, and pair-programming to drive

active adoption not just publication across the engineering team.

Define conformance criteria and lightweight review checkpoints so that new pipeline work

is assessed against framework standards before promotion to production.

Act as the technical authority and tiebreaker on engineering design decisions

establishing consistent patterns while preserving pragmatic flexibility where needed.

2. DataOps & CI/CD Pipeline Engineering

Design and implement CI/CD pipelines for data engineering workloads using GitHub

Actions or equivalent - covering lint, unit test, schema validation, and environment

promotion stages.

Establish automated unit testing patterns - including test coverage standards and

coverage reporting.

3. Data Quality & Observability Engineering

Implement data contract frameworks at ingestion, transformation, and consumption

boundaries - defining schemas, SLOs, and acceptable value ranges as code.

Build reusable data quality monitoring templates - parameterizable and composable

across data products.

Instrument pipelines with observability metadata: lineage, runtime metrics, freshness

timestamps, and row count deltas - surfaced into operational dashboards.

Design and test the incident response workflow for data quality breaches: automated

alerting, quarantine patterns, stakeholder notification, and self-healing logic where

feasible.

4. Snowflake Platform & Access Control Engineering

Design and implement scalable RBAC models in Snowflake - covering functional roles,

object ownership hierarchies, and data product consumer roles.

Build row-level security (RLS) frameworks using Snowflake row access policies - creating

reusable, metadata-driven policy templates that can be applied consistently across

Finance data products.

Define and implement dynamic data masking policies aligned to the data classification

taxonomy - ensuring sensitive financial data is protected at the platform layer, not just the

application layer.

Govern Snowflake resource utilization: warehouse sizing standards, query optimization

guidelines, and cost attribution tagging by domain or product.

5. GenAI-Augmented Data Engineering

Champion the exploration and adoption of GenAI tooling to amplify data engineering

productivity - including AI-assisted SQL/python code generation, automated

documentation, and intelligent pipeline debugging.

Prototype and evaluate LLM-powered data engineering assistants: natural language to SQL

interfaces, automated data contract generation, and AI-driven anomaly root cause

analysis.

Define guardrails and governance standards for GenAI use in data engineering workflows -

covering code review requirements, hallucination risk in data contexts, and audit

traceability.

Share findings and tooling recommendations with the wider data engineering community

through internal demos, documentation, and engineering blog posts.

6. Modernization & Delivery Velocity

Identify and eliminate sources of engineering friction - legacy patterns, manual

deployment steps, inconsistent environments - and replace with automated, standardsdriven equivalents.

Measure and report on delivery cycle time improvements attributable to framework

adoption: pipeline build time, time to production, defect escape rate, and time to recovery.

Lead or contribute to data engineering modernization initiatives: migrating legacy ETL

workloads, re-platforming to Snowflake, and adopting modern orchestration patterns.