Data Operations Analyst

Job Title: Data Operations Analyst

Location: India – Hyderabad

Experience: 9+ years

Technical Skills: Strong MS Excel skills, PL/SQL. Power Apps, Postgres/Snowflake, Power BI are nice to

have.

Role Overview

We are seeking a Data Integrity / Operations Analyst who goes far beyond traditional data entry.

This role is designed for a detail-oriented professional with strong critical thinking skills, a growth

mindset, and a desire to continuously improve how data is captured, validated, and used.

The successful candidate will not just input data, but will question its quality, identify patterns and

anomalies, suggest improvements, and help ensure data integrity across systems. This is an ideal

role for someone who enjoys structured work but also wants exposure to analytics, automation, and

data operations at scale.

Key Responsibilities

Data Ingestion & Processing

• Accurately ingest, update, and maintain large volumes of structured and semi-structured
  
  

data from multiple sources (spreadsheets, systems, documents, feeds).

• Follow defined data governance, quality, and security standards while ensuring timeliness
  
  

and accuracy.

• Perform initial data cleansing, normalization, and validation before data is consumed
  
  

downstream.

Data Quality & Validation

• Proactively identify inconsistencies, gaps, duplicates, and anomalies in incoming data.
• Apply logical checks and reconciliation techniques rather than relying solely on instructions.
• Escalate data quality issues with clear context, evidence, and suggested resolutions.
  
  

Critical Thinking & Continuous Improvement

• Question unclear or incomplete data where required.
• Suggest improvements to ingestion processes, templates, and validation rules.
• Identify repetitive manual tasks and partner with teams to streamline or automate them
  
  

where possible.

• Familiarity with AI tools and automation techniques to improve processes.
  
  

Stakeholder Interaction

• Strong communication skills when working with globally distributed teams and stakeholders
  
  

across multiple locations.

• Work closely with analysts, operations teams, and technology partners to clarify data
  
  

requirements.

• Translate ambiguous inputs into structured, usable datasets.
• Maintain clear documentation of data sources, assumptions, and known limitations.
  
  

Learning & Growth

• Continuously build knowledge of data tools, platforms, and best practices.
• Take ownership of personal development in areas such as data analytics, automation, or
  
  

reporting