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

Key Responsibilities

1. Data Pipeline & ETL/ELT Development

- Develop, optimize, and productionize Spark (PySpark/Scala) pipelines.
- Ingest, transform, cleanse, and aggregate large datasets from diverse 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.

1. 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.

1. 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.

1. 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.

1. Databricks (Added Advantage)

- Develop notebooks/jobs using PySpark/Scala.
- Manage clusters, workflows, and Delta Live Tables.
- Implement best practices for performance and cost efficiency.

1. SQL Engineering

- Strong command of SQL for aggregations, analytical functions, joins, profiling, and validation.
- Write and optimize complex queries supporting dashboards, metrics, and reports.

1. Cloud Data Platforms

Azure: Data Factory, Synapse Analytics, ADLS Gen2, Azure Functions (optional). Snowflake: Virtual Warehouses, Snowpipe, Streams & Tasks, performance tuning.
1. 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 programming skills: Spark, Scala, Python.
- 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.

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📌 Opportunity For Senior Data Engineer (Pune)
🏢 Hinduja Tech
📍 Pune