Data Engineer

Data Engineer

Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems.

The ideal candidate will have experience building:

• Reusable Python/PySpark frameworks for standardizing data engineering workflows
• Test frameworks to ensure pipeline reliability and correctness
• Data quality frameworks for monitoring and validation
• Additionally, hands-on experience with Datadog or similar observability tools is required to monitor pipeline performance, optimize resource usage, and ensure system reliability.

You will work within a cross-functional team, building scalable, production-grade data pipelines on cloud platforms such as AWS, Azure, or GCP.

Roles & Responsibilities

- Data Engineering & Framework Development

• Develop and maintain ETL/ELT pipelines in Databricks using PySpark and Python.
• Build reusable, modular frameworks to accelerate development and enforce standards across pipelines.
• Implement test frameworks for automated unit, integration, and regression testing of pipelines.
• Design and maintain data quality frameworks to validate ingestion, transformation, and output.
• Optimize Spark jobs for performance, scalability, and cost-efficiency.
• Collaborate with data architects to define robust data models and design patterns.

- Cloud & Platform Integration

• Integrate Databricks pipelines with cloud-native storage services (e.g., S3, ADLS, Snowflake).
• Implement CI/CD pipelines for Databricks notebooks and jobs using Git, Jenkins, or Azure DevOps.
• Ensure pipelines follow best practices for modularity, reusability, and maintainability.

- Monitoring, Observability & Optimization

• Use Datadog to monitor pipeline performance, resource utilization, and system health.
• Build dashboards and alerts for proactive monitoring and troubleshooting.
• Analyze metrics and logs to identify bottlenecks and improve reliability.

- Collaboration & Delivery

• Partner with data scientists, analysts, and business stakeholders to translate requirements into scalable solutions.
• Conduct code reviews, enforce best practices, and mentor junior engineers.
• Promote knowledge-sharing of reusable frameworks, testing practices, and data quality approaches.

Required Skills & Qualifications

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

5–8 years of experience in data engineering or software development.

3+ years hands-on experience with Databricks and PySpark.

Strong Python programming skills, including writing reusable libraries and frameworks.

Experience designing and implementing test frameworks for ETL/ELT pipelines.

Experience building data quality frameworks for validation, monitoring, and anomaly detection.

Proficiency in SQL and experience with cloud data warehouses (Snowflake, Redshift, BigQuery).

Familiarity with Datadog or similar monitoring tools for metrics, dashboards, and alerts.

Experience integrating Databricks with AWS, Azure, or GCP services.

Working knowledge of CI/CD, Git, Docker/Kubernetes, and automated testing.

Strong understanding of data architecture patterns — medallion/lakehouse architectures preferred.

Nice to Have

Experience with Airflow, Prefect, or Azure Data Factory for orchestration.

Exposure to infrastructure-as-code tools (Terraform, CloudFormation).

Familiarity with MLflow, Delta Live Tables, or Unity Catalog.

Experience designing frameworks for logging, error handling, or observability.

Knowledge of data security, access control, and compliance standards.

Strong problem-solving and analytical skills.

Excellent verbal and written communication.

The candidate should have a minimum of 5 years of experience in Large Language Models.

This position is based at our Bengaluru office.

A 15 years full-time education is required.

Ability to work in agile, cross-functional teams.

Ownership mindset, proactive, and self-driven.