Opportunity for Data Engineer

Hinduja Tech Limited; Pune District; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Hinduja Tech Limited is seeking an experienced Data Engineer to build and optimize large-scale data pipelines for a heavy analytics project. The role emphasizes writing complex aggregations, understanding business rules, and designing scalable data lake and data warehouse solutions across Databricks, Snowflake, and Azure ecosystems.

The ideal candidate will have hands-on experience with multiple cloud data platforms, strong SQL, and the ability to translate business requirements into technical

Qualifications

• Hands-on data engineering experience with analytics pipelines and data lake/warehouse concepts.
• Strong SQL skills including windowing and analytical functions.

Responsibilities

• Develop, optimize, and productionize Spark (PySpark/Scala) pipelines.
• Ingest, transform, cleanse, and aggregate large datasets from varied sources.
• Implement scalable ETL/ELT logic for batch and near-real-time pipelines.
• Apply best practices in partitioning, caching, and performance tuning.
• Write and validate complex aggregations and data models.

Skills

Spark

PySpark

Scala

Python

SQL

Databricks

Delta Lake

Snowflake

Azure

ADF/Synapse

Tools

Azure Data Factory

Azure Synapse

dbt

Airflow

Snowpipe

Job description

Data Engineer (8+ Years Experience) - Heavy Data Analytics Project

Tech Stack: Spark, PySpark, Scala, Python, SQL, Databricks, Data Lake, Data Warehouse, Snowflake, Azure (ADF/Synapse/ADLS)

Expected Skill set as per customer- Azure Databricks, Spark, Python, Delta Table, Snowflake basics, and prior experience on data ingestion/transformation.

About the Role

We are hiring a Data Engineer with strong hands-performance data pipelines for a heavy data analytics project. The candidate must be excellent at writing complex aggregations, understanding business processes and analytical requirements, and designing scalable data lake and data warehouse solutions. Experience across multiple data platforms (Databricks, Snowflake, Azure Data Factory, Synapse, etc.) is a strong advantage.

Key Responsibilities

• Develop, optimize, and productionize Spark (PySpark/Scala) pipelines.


• Ingest, transform, cleanse, and aggregate large datasets from varied sources.


• Implement scalable ETL/ELT logic for batch and near-real-time pipelines.


• Apply best practices in partitioning, caching, Delta Lake optimization, and performance tuning.

2. Heavy Data Analytics & Business Understanding

• Write complex aggregation logic (window functions, rollups, grouping sets, analytical functions).


• Understand business KPIs, metrics, and analytical use cases.


• Translate business needs into technical transformations and data models.


• Validate data outputs against business logic and analytics expectations.


• Collaborate with analysts on calculations: weekly/monthly aggregates, trend lines, performance metrics, dimensional rollups.


• Ensure accuracy, consistency, and traceability of business-critical metrics.

3. Data Lake Engineering

• Build and maintain multi-layer Data Lake architectures (Bronze/Silver/Gold).


• Work with Parquet, Delta Lake, ORC, and columnar storage formats.


• Implement schema evolution, auditing, and metadata strategies.

4. Data Warehouse Engineering

• Design dimensional models: Star Schema and Snowflake Schema.


• Build fact and dimension tables supporting analytics and reporting.


• Optimize table structures, keys, and partitioning strategies.

5. Databricks (Added Advantage)

• Manage clusters, workflows, and Delta Live Tables.


• Implement best practices for performance and cost efficiency.


• Strong command of SQL for aggregations, analytical functions, joins, profiling, and validation.


• Write and optimize complex queries supporting dashboards, metrics, and reports.

Snowflake: Virtual Warehouses, Snowpipe, Streams & Tasks, performance tuning.

8. Data Quality & Documentation

• Validate transformation logic against business rules.


• Document data flows, transformation rules, aggregation logic, and data dictionary/metadata.


• Work with QA and analysts to ensure outputs match business expectations.

Required Qualifications

• 5+ years of hands-on data engineering experience.


• Strong SQL skills (aggregations, analytical functions, large joins).


• Experience with Data Lake and Data Warehouse concepts.


• Experience with Spark-based processing (delta optimization, shuffle tuning, partitioning).


• Experience with at least one cloud data ecosystem (Azure/AWS/GCP).

Preferred Skills

• Experience with Databricks (highly desirable).


• Experience with Snowflake or modern cloud DWH.


• Experience with ADF/Synapse/Airflow/dbt for orchestration.


• Knowledge of CI/CD for data pipelines.


• Experience with large-scale data analytics environments.

Soft Skills

• Strong understanding of business logic behind analytics outputs.


• Ability to translate business metrics into technical transformations.


• Strong problem-solving and debugging skills.


• Good communication and cross-team collaboration.