Data Engineer

Valuenode Private Limited; India; Remote; INR 1,200,000 - 1,800,000; Full time

Job summary

Valuenode Private Limited is seeking a Data Engineer to design, build, and maintain scalable data pipelines from source systems into data lakes and warehouses. You will work with engineering, analytics, and business stakeholders to ensure accurate, accessible data with clear business logic.

The role emphasizes data modeling, data quality, and the ability to diagnose and resolve pipeline issues, using modern data tools and cloud platforms.

Qualifications

• 3+ years in data engineering or analytics engineering
• Strong SQL and data warehouse/lakehouse experience
• Python for data processing and automation
• Experience with orchestration and pipeline operations (scheduling, retries, SLAs)
• Strong fundamentals in data modeling, data quality, and debugging data issues
• Certifications or licenses: not required

Responsibilities

• Design, build, and maintain scalable data pipelines from source systems into data lakes and warehouses
• Develop and maintain data models and curated datasets for trusted reporting
• Implement data quality checks, monitoring, and alerting for reliability
• Collaborate with stakeholders to gather requirements and improve data platform performance

Skills

Data pipeline engineering

Data modeling

SQL optimization

Orchestration

Data quality

Observability

Tools

SQL

Python

Git

Airflow

dbt

Spark

Databricks

Job description

The Data Engineer designs, builds, and maintains reliable data pipelines and platforms thatpower analytics, reporting, and data products. You will partner with engineering, analytics,and business stakeholders to ensure data is accurate, accessible, well-modeled, and secure.

Key Responsibilities

• Design, build, and maintain scalable batch and or streaming data pipelines (ETL or ELT)from source systems into data lakes and warehouses.
• Develop and maintain data models and curated datasets that enable trusted reporting andanalytics, with clear definitions and consistent business logic.
• Implement data quality checks, monitoring, and alerting to ensure reliability and quicklydiagnose and resolve pipeline and data issues.
• Collaborate with cross-functional stakeholders to gather requirements, plan deliveries,and continuously improve data platform performance, cost, and usability.

Required Qualifications

• Experience: 3+ years in data engineering, analytics engineering, or backend engineeringwith strong data pipeline ownership.
• Core Skills:
  • Strong SQL and experience building datasets in a warehouse or lakehouse environment.
  • Proficiency in Python (or similar language) for data processing, automation, andpipeline development.
  • Experience with orchestration and pipeline operations (scheduling, retries, SLAs,incident troubleshooting).
  • Strong fundamentals in data modeling, data quality, and debugging complex data issues.
• Certifications or Licenses: Not required.

Preferred Qualifications

• Experience with modern data stacks and tools such as Airflow, dbt, Spark, Databricks, orsimilar technologies.
• Experience with cloud data platforms (AWS, GCP, or Azure) and common services for storage,compute, and warehousing.
• Familiarity with streaming technologies (Kafka, Kinesis, Pub/Sub) and event-driven datapatterns.
• Cloud or data engineering certifications (optional), and or strong evidence of advancedplatform ownership and delivery.

Skills and Competencies

• Hard Skills: data pipeline engineering, data modeling, SQL optimization, orchestration,data quality and observability.
• Soft Skills: communication, ownership and accountability, structured problem-solving,collaboration.
• Leadership: influence without authority, mentoring where applicable, proactive stakeholdermanagement.
• Behavioral Expectations: integrity and confidentiality, attention to detail, bias for action,client focus.

Tools, Tech, and Methods

• Primary Tools: SQL, Python, Git and code review, workflow orchestration, and a cloud datawarehouse or lakehouse platform.
• Tech Stack (optional): cloud services (AWS, GCP, Azure), storage (S3, GCS), warehouses(Snowflake, BigQuery, Redshift), transformation (dbt), compute (Spark, Databricks),streaming (Kafka), IaC (Terraform), CI and CD tooling.
• Methodologies: Agile delivery, DataOps practices, automated testing for pipelines, monitoringand alerting, performance and cost optimization.
• Documentation: maintain clear data contracts, model definitions, pipeline runbooks, andoperational notes in shared documentation (Notion, Confluence, Google Docs).
• Security or Privacy Tooling: follow access controls, least-privilege permissions, secretsmanagement, encryption, and secure data-sharing practices.

Security and Confidentiality

This role may involve access to confidential and sensitive data. Candidates must:

• Handle sensitive information with extreme care.
• Follow policies for data access, storage, and sharing.
• Maintain strict confidentiality and professional integrity in all situations.