Data Engineer II

Deutsche Telekom Digital Labs; New Delhi; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Deutsche Telekom Digital Labs in India seeks a hands-on Data Engineer II with strong GCP experience to design scalable data pipelines and reusable data-platform components, delivering production-grade data products across teams.

You will own end‑to‑end data processing—from design through testing, deployment, and production support—collaborating with data engineers, platform engineers, DevOps, and business stakeholders to optimize performance and cost.

Qualifications

• 4+ years of hands-on data engineering experience building production data solutions.
• Strong practical experience with Google Cloud Platform (GCP) and BigQuery.
• Advanced SQL skills and Python development for data processing, automation, and testing.

Responsibilities

• Design, develop, and maintain production-grade data pipelines on Google Cloud Platform.
• Build scalable batch and incremental data-processing solutions.
• Develop reusable transformation components, orchestration patterns, libraries, and templates.

Skills

GCP & BigQuery

SQL advanced

Python for data processing

ETL/ELT pipelines

Airflow / Cloud Composer

Data modelling

Data quality & testing

CI/CD in data pipelines

Tools

dbt / Dataform

Dataflow / Spark

Git

Terraform

Job description

We are looking for a hands-on Data Engineer II with strong Google Cloud Platform experience to build scalable data pipelines, transformations, and reusable data-platform capabilities. You will work across data engineering and data platform engineering, independently delivering production-grade data products while contributing reusable components and engineering patterns that can be adopted across multiple teams and use cases. The role requires strong hands-on development skills and the ability to take a solution from design through implementation, testing, deployment, and production support.

Responsibilities

• Design, develop, and maintain production-grade data pipelines on Google Cloud Platform.
• Build scalable batch and incremental data-processing solutions.
• Develop complex transformations using BigQuery, SQL, and Dataform/dbt.
• Build and maintain data-processing workflows using Cloud Composer / Apache Airflow.
• Develop reusable transformation components, orchestration patterns, libraries, and templates.
• Implement metadata-driven and configuration-driven processing where appropriate.
• Build distributed data-processing solutions using Dataflow / Apache Beam and/or Dataproc / Spark.
• Design data models for analytical and downstream data-product requirements.
• Implement incremental and idempotent processing patterns.
• Define and maintain schemas and data contracts.
• Implement automated data-quality checks, validation, and reconciliation.
• Capture and integrate metadata and lineage into data-processing workflows.
• Optimize BigQuery queries and pipelines for performance and cloud cost.
• Implement automated testing and integrate data workloads with CI/CD pipelines.
• Build monitoring and operational controls for production pipelines.
• Troubleshoot production issues and perform root-cause analysis.
• Contribute to reusable data-platform capabilities and engineering standards.
• Collaborate with data engineers, platform engineers, DevOps, architects, and business stakeholders.

Requirements

• 4+ years of hands-on data engineering experience building production data solutions.
• Strong practical experience with Google Cloud Platform (GCP).
• Strong hands-on experience with BigQuery.
• Advanced SQL skills, including complex joins, CTEs, window functions, and query tuning.
• Strong Python development skills for data processing, automation, and testing.
• Experience developing production ETL / ELT pipelines.
• Hands-on experience with Cloud Composer / Apache Airflow.
• Experience with Dataform and/or dbt.
• Experience designing batch and incremental processing pipelines.
• Experience with Dataflow / Apache Beam or Dataproc / Spark / PySpark.
• Strong understanding of data modelling, including normalization, denormalization, and dimensional modelling.
• Experience working with large-scale datasets.
• Experience with incremental and idempotent pipeline patterns.
• Understanding of data contracts and schema evolution.
• Experience implementing data-quality validation and reconciliation.
• Understanding of metadata and data lineage.
• Experience with Git, automated testing, and CI/CD.
• Experience troubleshooting and supporting production data pipelines.
• Ability to independently design solutions rather than only implement predefined specifications.

Technical Skills

• Cloud and Storage: Google Cloud Platform, BigQuery, Google Cloud Storage.
• Transformation and Orchestration: Advanced SQL, Dataform/dbt, Cloud Composer/Apache Airflow.
• Data Processing: Dataflow / Apache Beam, Dataproc / Spark / PySpark.
• Programming and Engineering: Python, Git, automated testing, CI/CD, monitoring, and troubleshooting.
• Data Engineering Capabilities: ETL/ELT, batch processing, incremental and idempotent pipelines, data modelling, data contracts, data quality and reconciliation, metadata and lineage, query performance optimization, cloud-cost optimization, and reusable data-platform components.

Good to Have

• Experience with Apache Iceberg or modern lakehouse table formats.
• Experience with streaming or event-driven processing.
• Practical understanding of Data Product / Data Mesh principles.
• Experience with metadata-driven or configuration-driven processing.
• Experience with Change Data Capture.
• Familiarity with Terraform / Infrastructure as Code.
• Experience building reusable components used by multiple engineering teams.

Strong Interpersonal Skills

• Strong ownership mindset and ability to take engineering work through production.
• Strong analytical, debugging, and problem-solving skills.
• Ability to communicate technical decisions clearly.
• Comfortable participating in design and code reviews.
• Ability to collaborate with engineering and business stakeholders.
• Ability to work independently while seeking guidance for complex architectural decisions.
• Comfortable working within distributed and multicultural teams.