Compliance Automation Engineer

Bee Talent Solutions; India; Hybrid; INR 800,000 - 1,200,000; Full time

Job summary

A talent acquisition firm in India seeks a Compliance Automation Engineer to enhance automation and operational efficiency in Third Party Risk Management. The role involves designing data pipelines, automating processes, and working with stakeholders on Snowflake and Python-based solutions. Ideal candidates should have a background in data engineering with strong technical skills in Python, Pyspark, and SQL, as well as experience with cloud environments and Jira. Competitive compensation offered.

Qualifications

• 3–5 years of relevant professional experience in data engineering or development roles.
• Strong hands-on experience in Python, Pyspark, and SQL.
• Experience working with Snowflake in data engineering projects.

Responsibilities

• Design, develop, and maintain data pipelines for various sources.
• Identify manual processes and implement automation solutions.
• Generate regular reports on pipeline performance and data quality.

Skills

Python

Pyspark

SQL

Data engineering

Snowflake

AWS

Jira

AI tools

Data visualization

Education

Bachelor’s or Master’s degree in Computer Science

Tools

Snowflake notebooks

Airflow

Docker

CI/CD

Kubernetes

Job description

We are seeking a hands‑on Compliance Automation Engineer to drive automation, workflow optimisation, and operational efficiency improvements across Third Party Risk Management (TPRM). The focus is on identifying manual processes, improving data reliability, automating recurring reporting, optimising Snowflake‑based workflows, and strengthening system integrations.

The ideal candidate combines strong data engineering fundamentals with a proactive automation mindset. You will work closely with Compliance, Risk, and Engineering stakeholders to design and implement solutions across Snowflake, Python‑based automation, and Jira workflow management.

Responsibilities

• Design, develop, and maintain data pipelines for collecting, processing, and analysing data from various sources.
• Identify manual processes and implement automation solutions to reduce operational effort.
• Replace manual Excel‑based tracking processes with scalable and structured solutions (e.g., Snowflake‑driven reporting, dashboards, or controlled Google Sheets integrations where appropriate).
• Leverage AI tools to accelerate development, documentation, and automation design.
• Apply best practices for data integrity, reliability, and scalability within the data pipeline.
• Work closely with engineering teams and stakeholders to design and implement practical, scalable solutions.
• Build and support automated solutions and infrastructure components across cloud environments.
• Ensure that data pipelines align with established standards, policies, and industry best practices.
• Collaborate with cross‑functional teams to implement necessary controls and reporting mechanisms.
• Convert business needs into functional requirements to create scalable, repeatable, and flexible pipelines.
• Maintain documentation of data pipeline architecture, configurations, and processes.
• Generate regular reports on pipeline performance, data quality, and operational metrics.

Requirements

• Bachelor’s or Master’s degree in Computer Science or equivalent.
• Minimum of 3–5 years of relevant professional experience in data engineering or development roles.
• Strong hands‑on experience in Python, Pyspark and SQL.
• Experience working with Snowflake in data engineering projects.
• Experience working with Snowflake notebo​oks and store procedures.
• Strong Python scripting skills for automation and integrations.
• Experience working within cloud environments (AWS).
• Experience configuring Jira workflows and automation rules.
• Familiarity with orchestration tools such as Airflow, Docker, CI/CD, or Kubernetes.
• Experience leveraging AI tools to enhance productivity, development workflows, or reporting processes.
• Experience working with data visualization tools (Hex).
• Experience working in regulated or audit‑sensitive environments (preferred).