Opportunity For Senior Data Engineer

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 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)

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

6. SQL Engineering

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

7. Cloud Data Platforms

Azure: Data Factory, Synapse Analytics, ADLS Gen2, Azure Functions (optional).

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 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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