Data Operations Engineer(DBT, Snowflake, Terraform, Apache, Python, AWS, SQL) Fulltime - Remote - Night Shift - 8+ Years needed

Mastek; Chennai District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Mastek in Chennai is seeking a data engineer to monitor and triage production data pipelines, build observability tooling, and collaborate with platform, data, and analytics teams.

You will participate in on-call rotations, manage IaC configurations with Terraform, and support onboarding, RBAC, and provisioning across data platforms while ensuring data quality and lineage across Snowflake, dbt, and Airflow.

Qualifications

• 2–4 years in data engineering, DevOps, or data operations.
• Solid understanding of Snowflake, dbt, Airflow, Fivetran and cloud storage.
• SQL proficiency and ability to debug transformations/analytic queries.
• Basic scripting for automation; Python or Bash.
• Git version control and CI/CD pipelines for data projects.
• Strong troubleshooting and communication skills.
• Experience with infrastructure-as-code (Terraform, CloudFormation).
• Familiarity with observability tools like Datadog.
• Exposure to data governance concepts and data lineage.
• Knowledge of ELT and schema evolution in distributed systems.

Responsibilities

• Monitor and triage production data pipelines, ingestion jobs, and transformations.
• Manage and resolve data incidents, working with platform, data and analytics teams.
• Develop internal tools/scripts for observability and automation.
• Participate in on-call rotations to support uptime and SLAs.
• Assist with IaC configurations (Terraform for Snowflake, AWS, Airflow).
• Support onboarding, RBAC, and provisioning across data platforms.
• Assist schema changes, versioning, and documentation.
• Set up monitoring on new pipelines in metaplane.
• Diagnose model failures and upstream data issues.
• Collaborate with analytics to validate data freshness, quality, and lineage.
• Coordinate backfills, schema adjustments, and reprocessing as needed.
• Manage operational aspects of source ingestion (REST APIs, batch jobs, Kafka).

Skills

Data engineering

SQL

Python

Bash

Git

Tools

Snowflake

dbt

Airflow

Fivetran

Terraform

CloudFormation

Datadog

Job description

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

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

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)

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