Data Operations Engineer

3shool Technology Consultants; Pune District, Mumbai, Chennai District; Hybrid; INR 2,500,000 - 3,000,000; Full time

Job summary

3shool Technology Consultants is seeking a Data Operations Engineer to support and optimize data pipelines across Snowflake, dbt, Airflow, and Fivetran within a hybrid, multi-location setup. The role emphasizes reliability, observability, and scalable data infra.

You will monitor, triage, and automate data workflows, collaborate with analytics teams, and help manage ML/DS infrastructure as needed. Strong scripting and cloud tooling experience are essential.

Qualifications

• 2–4 years of experience in data engineering, DevOps, or data operations.
• Proficiency with Snowflake, dbt, Airflow, Fivetran and cloud storage.
• Strong SQL skills and ability to debug data transformations.

Responsibilities

• Monitor and triage production data pipelines, ingestion jobs, and transformation workflows.
• Manage and resolve data incidents and operational issues with cross-functional teams.
• Develop and maintain internal tools for observability and automation of data workflows.
• Participate in on-call rotations to support platform uptime and SLAs.

Skills

SQL

Troubleshooting

Communication

Automation

Tools

Snowflake

dbt

Airflow

Fivetran

Cloud storage

Terraform

CloudFormation

DataDog

Python

Bash

CI/CD

Git

Job description

Data Operations Engineer (Snowflake, DBT, Airflow, Fivetran, cloud storage, CI/CD, Python OR Bash, Terraform, CloudFormation, DataDog)

Location: Any Mastek Office Location Hybrid

30 LPA Max per Annum

What Youll Do

Operations Support

• Monitor and triage production data pipelines, ingestion jobs, and transformation workflows (e.g. dbt, Snowflake tasks)
• Manage and resolve data incidents and operational issues, working cross-functionally with platform, data, and analytics teams
• Develop and maintain internal tools/scripts for observability, diagnostics, and automation of data workflows
• Participate in on-call rotations to support platform uptime and SLAs

Data Platform Engineering Support

• Help manage infrastructure-as-code configurations (e.g., Terraform for Snowflake, AWS, Airflow)
• Support user onboarding, RBAC permissioning, and account provisioning across data platforms
• Assist with schema and pipeline changes, versioning, and documentation
• Assist with setting up monitoring on new pipelines in metaplane

Data & Analytics Engineering Support

• Diagnosing model failures and upstream data issues
• Collaborate with analytics teams to validate data freshness, quality, and lineage
• Coordinate and perform backfills, schema adjustments, and reprocessing when needed
• Manage operational aspects of source ingestion (e.g., REST APIs, batch jobs, database replication, kafka)

(confirm the writeup with Jason Prentice)

ML-Ops & Data Science Infrastructure

• Collaborate with the data science team to operationalize and support ML pipelines, removing the burden of infrastructure ownership from the team
• Monitor ML batch and streaming jobs (e.g., model scoring, feature engineering, data preprocessing)
• Maintain and improve scheduling, resource management, and observability for ML workflows (e.g., using Airflow, SageMaker, or Kubernetes-based tools)
• Help manage model artifacts, metadata, and deployment environments to ensure reproducibility and traceability
• Support the transition of ad hoc or experimental pipelines into production-grade services

What We’re Looking For

Required Qualifications

• At least 2–4 years of experience in data engineering, DevOps, or data operations roles
• Solid understanding of modern data stack components (Snowflake, dbt, Airflow, Fivetran, cloud storage)
• Proficiency with SQL and comfort debugging data transformations or analytic queries
• Basic scripting/programming skills (e.g., Python, Bash) for automation and tooling
• Familiarity with version control (Git) and CI/CD pipelines for data projects
• Strong troubleshooting and communication skills — you enjoy helping others and resolving issues
• Experience with infrastructure-as-code (Terraform, CloudFormation)
• Familiarity with observability tools such as datadog
• Exposure to data governance tools and concepts (e.g., data catalogs, lineage, access control)
• Understanding of ELT best practices and schema evolution in distributed data systems