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
