How do I apply for Data Engineer- GCP/Azure at Innovantes?

Open this job on Feedback AI and sign up free (or sign in). You can apply from the job detail page; match scores and skill gaps appear when your profile is complete. Innovantes may attach structured assessments to interview rounds after you apply.

Does Feedback AI show a match score for this Data Engineer- GCP/Azure role?

Yes after you add a resume or skills. Match score ranks this role against your profile — skills met, gaps, level, and location — so you prioritise applications where fit is strongest.

Is Data Engineer- GCP/Azure remote or on-site in Chandigarh?

This listing is marked on_site with location Chandigarh. Confirm details on the job description before applying.

What skills matter for this Data Engineer- GCP/Azure role at Innovantes?

This is a mid-level Data Engineer- GCP/Azure role. Key skills include skills listed in the job description. Feedback AI highlights matched vs missing skills on your profile after signup.

Data Engineer- GCP/Azure at Innovantes

Back to Browse Jobs

Innovantes

Create a free account

One signup unlocks everything for this job:

See your personalized profile match score
See the skills this role is missing for you
Apply to this job and track your application
Generate an AI cover letter for this role

See my match score

Innovantes

Chandigarh · On-site · 1-3 Yrs · 6 days ago

1 open role1 posted in 7d1 in 30d

Hiring: Engineering

Job description

Overview

Key Responsibilities

Responsibilities

Data Architecture and Pipeline Design
Architect end-to-end data pipelines supporting enterprise BI, reporting, analytics, and data warehousing requirements.
Design scalable data ingestion, transformation, storage, and serving architectures across multiple data sources and destinations.
Define appropriate approaches for batch processing, incremental ingestion, CDC, historical data loads, and data synchronization.
Design data lake and lakehouse architectures, including raw, curated, and analytics-ready data layers.
Make technical decisions on processing frameworks, storage formats, orchestration, data modelling, and pipeline reliability.
Build integrations across heterogeneous enterprise systems, including relational databases, cloud platforms, APIs, SaaS applications, files, and legacy systems.
Handle diverse data formats, inconsistent schemas, changing source structures, large data volumes, and complex transformation requirements.
Implement incremental extraction, change detection, deduplication, reconciliation, and cross-source data consistency.
Resolve integration challenges involving API pagination, rate limits, authentication, schema evolution, late-arriving data, and failed or partial loads.
Develop reusable ingestion frameworks and standardized integration components to accelerate future implementations.
Design and implement cloud-based data platforms using GCP and/or Microsoft Azure.
Develop data pipelines using technologies such as PySpark, Apache Spark, BigQuery, Cloud Storage, Dataproc, Cloud Data Fusion, Azure Data Factory, Azure Data Lake Storage, and Azure Databricks.
Integrate cloud storage, processing engines, relational databases, and downstream analytics platforms.
Evaluate technology choices based on scalability, maintainability, security, processing performance, and cloud costs.
BI and Analytics Data Platforms
Build and maintain curated datasets and analytical data models for Power BI and other BI platforms.
Design data structures that support reporting performance, historical analysis, dimensional modelling, and consistent business metrics.
Understand the complete data lifecycle from source-system extraction to transformation, warehouse modelling, and BI consumption.
Work with analytics teams to translate reporting requirements into robust, reusable data pipelines.
Optimize data refresh strategies, incremental processing, and downstream query performance.
PySpark and Distributed Processing
Develop production-grade data processing applications using Python, PySpark, and Apache Spark.
Optimize large-scale transformations, joins, shuffles, partitioning, caching, and file layouts.
Troubleshoot memory issues, skewed data, slow-running jobs, and distributed processing failures.
Work with Parquet, Avro, JSON, CSV, and other structured or semi-structured formats.
Apply sound engineering practices to build modular, testable, maintainable pipeline components.
Own pipelines throughout their lifecycle: design, development, deployment, monitoring, optimization, and support.
Implement orchestration, dependency management, retries, checkpoints, idempotency, and recovery mechanisms.
Set up logging, alerting, data quality validation, reconciliation, and failure notifications.

Verify Python for this role

Free PRACTICE for Python adds a verified skill signal that can boost how roles like this rank for you — optional, not required to browse or apply.

Want match scores and deeper insight? Create a free candidate profile

Job application FAQ

Last updated 27 October 2026

How do I apply for Data Engineer- GCP/Azure at Innovantes?: Open this job on Feedback AI and sign up free (or sign in). You can apply from the job detail page; match scores and skill gaps appear when your profile is complete. Innovantes may attach structured assessments to interview rounds after you apply.
Does Feedback AI show a match score for this Data Engineer- GCP/Azure role?: Yes after you add a resume or skills. Match score ranks this role against your profile — skills met, gaps, level, and location — so you prioritise applications where fit is strongest.
Is Data Engineer- GCP/Azure remote or on-site in Chandigarh?: This listing is marked on_site with location Chandigarh. Confirm details on the job description before applying.
What skills matter for this Data Engineer- GCP/Azure role at Innovantes?: This is a mid-level Data Engineer- GCP/Azure role. Key skills include skills listed in the job description. Feedback AI highlights matched vs missing skills on your profile after signup.

Job description

Overview

Responsibilities

Job application FAQ

Explore related listings