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Kumaran Systems

Data Engineer

Kumaran Systems Hyderabad, Telangana, India

IT Services and IT Consulting · 1,001-5,000 employees

Jun 27
data-engineer Senior (5-10 yrs) Full-time India
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About the role

Design, build, and optimize Azure data pipelines and lakehouse solutions while ensuring secure, reliable data delivery. Collaborate across teams to maintain high standards of governance, automation, and documentation in an Agile environment.

What they look for

Azure Data Factory Databricks Python Spark Pandas Delta Lake SQL Azure Data Lake Storage Data Modeling ETL ELT CI/CD Azure DevOps Generative AI Agile Scrum

Requirements

Requires 6-10 years of experience in data engineering with strong proficiency in Databricks, Spark, Python, and Azure data services. Candidates must have experience with Delta Lake architecture, SQL, CI/CD practices, and the ability to mentor junior team members.

Full description

Data Engineer/Developer

Role Summary Design, build, and optimize Azure data pipelines and lakehouse solutions. Deliver secure, reliable datasets with strong governance, automation, and documentation. Collaborate across teams and contribute to standards in an Agile setting.

Must-Have (Day 1)

  • Experience:

4–6 years in data engineering

  • Core

Platform: Databricks with Python, Spark, Pandas (notebooks and modular code)

  • Orchestration:

Azure Data Factory (pipelines, integration runtimes); ingest from diverse sources

  • Lakehouse:

Delta Lake fundamentals; Medallion architecture (bronze/silver/gold) in production

  • Storage/SQL/Performance:

Azure Data Lake Storage (ADLS); strong SQL; performance-aware design

  • Data

Patterns: ETL/ELT; data modeling (e.g., dimensional/star schema)

  • DevOps

& Security: CI/CD for data projects (Azure DevOps or GitHub Enterprise); familiarity with Azure Entra ID for SSO/RBAC; secure workspace/data access

  • Quality

& Observability: Data validation/testing, code reviews, and basic monitoring/alerting for jobs/pipelines

  • Ways of

Working: Agile/Scrum (Jira/Confluence); clear pipeline and data contract documentation

  • Collaboration:

Effective stakeholder engagement; support/mentor junior team members; clear communication

  • Generative

AI (Day 1):

  • Prompt

design for data tasks (ingestion, transformations, documentation) with clear objectives and constraints

  • Use of

Copilot/ChatGPT to scaffold notebooks/jobs, generate tests, and optimize SQL/Spark—validates outputs before merging

Nice-to-Have (Train within 60–90 days)

  • Unity

Catalog migration (Hive to Unity) and permissions/governance

  • Databricks

DevOps (cluster configuration, secret management, workspace automation)

  • Azure

Functions (C# or Python) for orchestration/integration

  • Synapse

dedicated SQL pools or dbt; Delta Live Tables

  • Financial

services domain exposure

Shared Expectations

  • Work

independently with minimal supervision while contributing to team outcomes

  • Commitment

to secure practices and production-grade reliability

  • Continuous

improvement mindset and willingness to learn new tools/technologies

  • Willingness

to work within regulated environment controls and policies

  • Use

Generative AI responsibly to improve velocity and quality (simple, structured prompts; guardrails; validate AI-assisted outputs before adoption)

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