Senior Data Engineer / Databricks Engineer

Role Overview

We are looking for a Senior Data Engineer / Databricks Engineer with 8+ years of experience to design, develop, optimize, and support enterprise data pipelines and analytical datasets.

The candidate will work with Azure Databricks, Azure Data Lake, Azure Data Factory, PostgreSQL, and Azure DevOps to deliver reliable data ingestion, transformation, reconciliation, and reporting-ready data solutions.

Key Responsibilities

• Design, develop, and maintain ETL/ELT pipelines for data ingestion, transformation, and integration.

• Develop Databricks notebooks and reusable data engineering components using PySpark and SQL.

• Implement Medallion Architecture (Bronze, Silver, and Gold layers).

• Integrate operational, financial, accounting, portfolio, risk, and enterprise data sources.

• Develop curated analytical datasets and data models for reporting and analytics.

• Implement data-quality checks, validation rules, reconciliation controls, and exception handling.

• Optimize Databricks workloads, SQL queries, and PostgreSQL-related data processing.

• Monitor scheduled data loads and troubleshoot pipeline failures and production incidents.

• Support data lineage, metadata, source-to-target mapping, and technical documentation.

• Implement CI/CD, source control, deployment validation, and release-management processes.

• Collaborate with architects, BI developers, QA analysts, business analysts, and DevOps teams.

• Maintain pipeline documentation, operational runbooks, and monitoring reports.

Mandatory Skills & Experience 

• 8+ years of overall IT experience, with strong relevant Data Engineering experience.

• Azure Databricks — notebooks, transformations, and pipeline development.

• Azure Data Lake Storage (ADLS).

• PySpark, Python, and SQL.

• ETL/ELT pipeline development and data integration.

• Medallion Architecture — Bronze, Silver, and Gold layers.

• Strong SQL and PostgreSQL skills.

• Data quality, reconciliation, validation, and exception handling.

• Performance tuning and production troubleshooting.

• Data pipeline monitoring and scheduled job support.

Preferred Skills

• Azure Data Factory (ADF).

• Azure DevOps, Git, CI/CD pipelines, and automated deployments.

• Data lineage, metadata management, and source-to-target mapping.

• Delta Lake and lakehouse data modeling.

• Batch processing, job recovery, and operational automation.

• Financial services, accounting, portfolio, risk, or reinsurance data.

• Release management, root-cause analysis, and production support.