Data Analyst

Claidroid

Pune City, Maharashtra, India

Data Analyst / Analytics Engineer – Azure Synapse & Data Platform

Claidroid — Pune / Thiruvananthapuram, India (Hybrid)

Claidroid Technologies Pvt. Ltd.

Location: Pune / Trivandrum

Work Mode: Hybrid / Project Delivery Model

Experience: 5–8+ Years

We’re Hiring: Data Analyst / Analytics Engineer – Azure Synapse & Data Platform

Claidroid Technologies Pvt. Ltd. is hiring an experienced Data Analyst / Analytics Engineer for a strategic enterprise data platform engagement with a leading global BFSI client.

The role focuses on analysing, validating and improving enterprise-scale data products built on Microsoft Azure Synapse Analytics, Azure Data Lake Storage Gen2, Delta Lake and Medallion/Lakehouse architectures.

This opportunity is ideal for professionals with exceptional SQL expertise, strong data engineering fundamentals and hands-on experience in data lineage, data profiling, data quality assessment and analytical data modelling.

This is not a traditional reporting-focused Data Analyst role.

We are seeking a technically strong professional who can investigate complex data environments, understand underlying data architecture, trace data movement across multiple platform layers and identify inconsistencies between source systems and downstream data products.

Candidates with strong knowledge of Insurance Domain data and enterprise financial services platforms are particularly encouraged to apply.

About Claidroid Technologies

Claidroid Technologies Pvt. Ltd. is a global digital transformation and technology services partner built around Cloud, AI, Automation and Enterprise Platforms.

We work with leading enterprises to modernize technology platforms, improve operational efficiency, strengthen governance and deliver high-quality technology solutions.

Our expertise spans Cloud Engineering, Data Engineering, Analytics, Artificial Intelligence, Intelligent Automation, DevOps, DevSecOps, AIOps and Enterprise Digital Transformation.

Role Overview

As a Data Analyst / Analytics Engineer, you will work within an established enterprise data platform team to investigate, validate and improve foundational and business-specific data products.

You will collaborate closely with senior data engineers, solution architects, business stakeholders and analytics teams to ensure that data produced by the platform is complete, accurate, consistent and aligned with business requirements.

The role requires strong hands-on expertise in advanced SQL, Azure Synapse Analytics, data profiling, data lineage investigation and Medallion/Lakehouse architecture.

You will examine data across multiple architectural layers, from raw ingestion through harmonized, conformed and consumption datasets, identifying gaps, unexpected transformations and data quality issues.

You will also contribute to data governance, analytical modelling, semantic datasets and platform improvement initiatives.

Key Responsibilities

Advanced SQL & Data Investigation

Develop complex SQL queries to interrogate and validate enterprise data products.
Analyse large and complex datasets across multiple platform layers.
Use advanced SQL techniques, including multi-table joins, Common Table Expressions (CTEs), window functions, aggregations and recursive queries.
Investigate unfamiliar or poorly documented datasets to understand their structure, relationships and business meaning.
Compare source data with transformed and downstream datasets to identify discrepancies.
Validate data completeness, correctness and consistency across foundational and use-case data products.
Investigate unexpected data behaviour and identify root causes.
Design SQL-based tests to validate data quality hypotheses and transformation logic.

Azure Synapse & Enterprise Data Platform

Work extensively with Microsoft Azure Synapse Analytics.
Query and investigate datasets using Azure Synapse SQL Pools and Serverless SQL.
Analyse data stored in Azure Data Lake Storage Gen2.
Work with Delta Lake, Parquet files and distributed data processing environments.
Understand the structure and purpose of different layers within enterprise data platforms.
Investigate data movement from ingestion through transformation and consumption layers.
Review Spark-generated outputs and their integration with analytical datasets.
Collaborate with data engineering teams to identify opportunities for platform improvements.

Data Lineage & Medallion Architecture

Trace end-to-end data lineage across complex enterprise data pipelines.
Understand data flow through Raw, Harmonized, Conformed and Consumption layers.
Identify where data originates, how it is transformed and how it reaches downstream analytical products.
Investigate missing, incomplete or incorrectly transformed data.
Validate consistency between source systems, intermediate datasets and final consumption models.
Understand Change Data Capture (CDC) and Slowly Changing Dimension (SCD) patterns.
Assess how transformation rules affect historical data and downstream reporting.
Support data platform re-engineering and migration decisions through evidence-based analysis.

Data Quality, Profiling & Gap Analysis

Perform comprehensive data profiling across enterprise datasets.
Conduct null-value analysis, cardinality checks, referential integrity validation and data distribution analysis.
Identify data anomalies, unexpected transformations, duplicate records and quality inconsistencies.
Develop structured gap analyses comparing expected business outcomes against actual platform outputs.
Document data discrepancies and provide actionable findings to engineering teams.
Support the development of data quality rules and validation frameworks.
Contribute to data governance initiatives through dataset profiling and quality assessments.
Identify systemic data quality issues affecting downstream analytics and business decisions.

Data Modelling & Analytics Engineering

Analyse and validate analytical data models used by enterprise reporting and analytics applications.
Work with surrogate keys, conformed dimensions, denormalization and Slowly Changing Dimensions.
Validate business logic embedded within data transformation layers.
Compare expected and actual results across analytical datasets.
Contribute to the development of semantic views and analytical datasets.
Support self-service analytics and reporting capabilities where required.
Understand how Power BI semantic models consume underlying data products.
Translate business requirements into structured analytical data models and validation approaches.

Business Collaboration & Technical Documentation

Work closely with senior data engineers and solution architects to investigate platform-level data issues.
Collaborate with business stakeholders to understand expected data behaviour and business logic.
Translate technical data findings into clear business-relevant observations.
Prepare data quality assessments, gap analyses, investigation reports and data dictionaries.
Provide evidence-based recommendations supporting platform re-engineering and migration.
Communicate data inconsistencies clearly to technical and non-technical stakeholders.
Take ownership of assigned investigations while collaborating effectively with engineering teams.

Must-Have Skills

SQL & Data Querying

Expert-level SQL, including complex joins, CTEs, window functions, aggregations and recursive logic.
Strong experience analysing and profiling large enterprise datasets.
Ability to reverse-engineer data transformations and investigate undocumented datasets.
Experience with partitioned tables and distributed query engines.

Azure Data Platform

Strong experience with Microsoft Azure Synapse Analytics.
Azure Synapse SQL Pools and/or Serverless SQL.
Azure Data Lake Storage Gen2.
Delta Lake, Parquet and distributed data environments.
Understanding of modern enterprise data platform architecture.

Data Architecture & Lineage

Strong understanding of Medallion/Lakehouse architecture.
Knowledge of Raw, Harmonized, Conformed and Consumption data layers.
Experience tracing end-to-end data lineage.
Understanding of CDC and SCD patterns.
Knowledge of dimensional modelling and data transformation logic.

Data Quality & Governance

Hands-on experience in data profiling and validation.
Data quality analysis, gap identification and discrepancy investigation.
Cross-system and cross-layer data reconciliation.
Knowledge of data quality frameworks and rule-based validation.
Understanding of data governance principles.

Analytics & Business Intelligence

Experience with Power BI or equivalent BI tools.
Understanding of semantic models and analytical datasets.
Ability to translate business questions into analytical data requirements.

Domain & Professional Skills

Knowledge of Insurance Domain data and business processes.
Strong analytical, investigative and problem-solving skills.
Excellent documentation and stakeholder communication.
Ability to collaborate with data engineering teams, solution architects and business users.
Experience in complex enterprise-scale data environments.

Good-to-Have Skills

Python / Pandas: Data exploration, profiling and validation.
PySpark: Investigation of large-scale distributed datasets.
dbt: Analytics engineering and transformation model documentation.
Microsoft Purview: Data cataloguing, governance and lineage management.
Unity Catalog: Metadata management and data governance.
Data Observability: Monitoring data quality and reliability.
Insurance Domain: Policy, sales, CRM and related insurance data structures.
Financial Services: Experience in BFSI data platforms.
Data Migration: Data reconciliation and validation during platform modernization.

Candidate Profile

We are looking for technically strong Data Analysts / Analytics Engineers who can go beyond reporting and independently investigate complex enterprise data environments.

The ideal candidate should have:

5–8+ years of relevant experience in Data Analysis, Analytics Engineering or technically focused BI Engineering.
Exceptional SQL expertise with the ability to independently interrogate complex datasets.
Strong knowledge of Azure Synapse Analytics and modern Lakehouse architectures.
Experience investigating data quality, lineage, transformation and reconciliation issues.
Solid understanding of data engineering concepts and enterprise data platform architecture.
Ability to navigate undocumented or legacy datasets independently.
Experience preparing structured gap analyses, data dictionaries and investigation findings.
Understanding of Power BI semantic models and analytical data consumption.
Knowledge of Insurance Domain data and business processes.
Strong stakeholder communication and problem-solving capabilities.

Candidates should be comfortable working at the intersection of Data Engineering, Data Analytics, Enterprise Architecture and Business Data Validation.

Why Join Claidroid Technologies?

At Claidroid Technologies, you will work on enterprise-scale data transformation initiatives for global clients in an environment that values engineering excellence, analytical thinking, technical ownership and continuous improvement.

This role provides strong exposure to:

Microsoft Azure Synapse Analytics
Azure Data Lake Storage Gen2
Advanced SQL & Data Investigation
Enterprise Data Engineering
Medallion / Lakehouse Architecture
Delta Lake & Parquet
End-to-End Data Lineage
Data Profiling & Data Quality
Data Governance & Validation Frameworks
Data Modelling & Semantic Layers
Power BI & Enterprise Analytics
CDC / SCD Data Patterns
Data Platform Re-engineering
Enterprise Data Migration
Insurance & Financial Services Data
Global Enterprise Technology Engagements

Work Location

Pune / Trivandrum – Hybrid

Applications are welcome from candidates across India who are willing to work from the designated project location as required by the client and project delivery model.

Apply Now

If you are passionate about Advanced SQL, Azure Synapse, Data Engineering, Data Lineage and solving complex enterprise data challenges, we would like to hear from you.

Join Claidroid Technologies and contribute to building reliable, high-quality enterprise data platforms for global clients.

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