Bhosari Pune Maharashtra Engineer technical publication Jobs – 48 Open Jobs Today

Principal Data Engineer
Eaton —Pune, Maharashtra
- Microsoft Access
- Computer Science
- Master's degree
Java Full Stack Developer
Barclays —Pune, Maharashtra
- Java
- Master's degree
- AWS
Computational Aeroelasticity Developer
Cadence Design Systems —Pune, Maharashtra
- Full-time
- Doctoral degree
- Research
- Master's degree
8d
React Native Engineer
BigBinary —Pune, Maharashtra
- Remote
- English
Quick apply
Application Support Engineer
E Tech Group —Pune, Maharashtra
- Engineering
- Electrical experience
- Bachelor's degree
Quick apply
Principal Cloud Engineer
ACI Worldwide —Pune, Maharashtra
- Full-time
- Azure
- Computer Science
- DevOps
7d
Senior Software Engineer (Java & Agentic AI)
Securonix —Pune, Maharashtra
- Full-time
- Hybrid work
- Azure
- Java
- SQL
- Health insurance
Quick apply
Senior Software Engineer - cloud
Eaton —Hadapsar, Pune, Maharashtra
- Azure
- Java
- Master's degree
2d
Senior Software Engineer
Playlist —Pune, Maharashtra
- Rotational shift
- Remote
- Computer Science
- DevOps
- Databases
Quick apply
Staff Engineer - SiC Technologist
NAVITAS SEMICONDUCTOR USA, INC. —Pune, Maharashtra
- Doctoral degree
- Master's degree
- Electrical experience
Quick apply
Senior Device Engineer
NAVITAS SEMICONDUCTOR USA, INC. —Pune, Maharashtra
- Doctoral degree
- Research
- Master's degree
Quick apply
Senior Quantitative Scientist – Grid Behavioral Modeling
Siemens —Pune, Maharashtra
- Full-time
- Doctoral degree
- Computer Science
- Research
Lead System Engineer - AWS IAM, Python and Agentic AI
EPAM Systems, Inc. —Pune, Maharashtra
- English
- AWS
- IT
- Health insurance
- Paid time off
Solution Architect, Process Intelligence
Edwards Lifesciences —Pune, Maharashtra
- Full-time
- Azure
- Oracle
- Computer Science
AI / ML Engineer
Accenture —Pune, Maharashtra
- Full-time
- Machine learning
- AI
Principal Software Engineer (.NET)
ACI Worldwide —Pune, Maharashtra
- Full-time
- Azure
- Computer Science
- AWS
7d
Lead Engineer- Reliability
Eaton —Hadapsar, Pune, Maharashtra
- Master's degree
- Electrical experience
- Bachelor's degree
Principal Engineer (VP)
Barclays —Pune, Maharashtra
- System design
- Master's degree
- AWS
Carbonate Reservoir Sedimentologist - TSI
bp —Pune, Maharashtra
- Full-time
- Hybrid work
- Doctoral degree
- Master's degree
- Project management
7d
Data Science - II
ProcDNA —Pune, Maharashtra
- Full-time
- Azure
- Computer Science
- Microsoft Office

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Principal Data Engineer

Eaton -
Pune, Maharashtra

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Job details

Qualifications

CI/CD
Microsoft Access
Law
Computer Science
Git
Master's degree
SQL
RBAC
Continuous integration
Macros
GitHub
Unit testing
AI
Python

Full job description

What you’ll 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. In Eaton, making our work exciting, engaging, meaningful; ensuring safety, health, wellness; and being a model of inclusion & diversity are already embedded in who we are - it’s in our values, part of our vision, and our clearly defined aspirational goals. This exciting role offers opportunity to:

The principal 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 forward-looking 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
10+ 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 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
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.

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, standards-driven 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.

Apply Now

Refine Your Search

engineer technical publication jobs in bhosari pune maharashtra

Principal Data Engineer

Java Full Stack Developer

Computational Aeroelasticity Developer

React Native Engineer

Application Support Engineer

Principal Cloud Engineer

Senior Software Engineer (Java & Agentic AI)

Senior Software Engineer - cloud

Senior Software Engineer

Staff Engineer - SiC Technologist

Senior Device Engineer

Senior Quantitative Scientist – Grid Behavioral Modeling

Lead System Engineer - AWS IAM, Python and Agentic AI

Solution Architect, Process Intelligence

AI / ML Engineer

Principal Software Engineer (.NET)

Lead Engineer- Reliability

Principal Engineer (VP)

Carbonate Reservoir Sedimentologist - TSI

Data Science - II

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