Data Engineer

Data Engineering & Pipeline Development:

• Design, develop, and maintain end-to-end data pipelines in Databricks using Spark and Delta Lake
• Build and optimize ELT/ETL processes for structured and unstructured data ingestion into the Data Lakehouse
• Implement scalable ingestion patterns (batch and event-driven) from internal systems, third-party APIs, and cloud sources
• Develop data models (bronze, silver, gold layers) to support enterprise reporting, analytics, and downstream consumption

Data Platform & Integration:

• Integrate the Data Lakehouse with enterprise tools such as Tableau, Alteryx, and machine learning platforms
• Design and implement data access controls, identity management, and secure data sharing mechanisms
• Support API-based integrations and downstream data consumption patterns

Data Quality, Governance & Controls:

• Implement data quality checks, reconciliation processes, and monitoring within Databricks pipelines
• Ensure adherence to enterprise data governance standards, including lineage, metadata, and audit requirements
• Support regulatory and compliance requirements (e.g., data integrity, privacy, and security controls)

Cloud & Automation:

• Develop and manage workflows using orchestration tools (e.g., Airflow, Control-M)
• Automate data pipelines, deployments, and operational processes through CI/CD pipelines
• Leverage cloud-native services (AWS/Azure) for data processing, storage, and event-driven architectures
• Operations & SupportMonitor, troubleshoot, and optimize data pipelines and Spark workloads for performance and reliability
• Support production data platforms, including incident resolution and root cause analysis
• Ensure high availability, data integrity, and SLA adherence across enterprise data systems

Collaboration:

• Partner with data architects, data scientists, BI teams, and business stakeholders to deliver data solutions
• Participate in Agile ceremonies and contribute to iterative delivery of data products
• Translate business requirements into scalable technical data solutions

• Required Qualifications3+ years of experience in data engineering, data platforms, or related roles
• Hands-on experience with Databricks, Apache Spark (PySpark), and Delta Lake
• Strong SQL and data modeling skills (relational and dimensional)
• Experience building and supporting data pipelines in a cloud environment (AWS or Azure)
• Experience with ELT/ETL tools (e.g., Fivetran, custom ingestion frameworks)
• Familiarity with data orchestration tools (Airflow, Control-M)
• Experience working in Agile development environments, * Experience in financial services or regulated environments (e.g., banking, risk, regulatory reporting)
• Knowledge of data governance frameworks and tools (e.g., Collibra)
• Experience with real-time or streaming data pipelines
• Exposure to machine learning pipelines and feature engineering in Databricks
• Cloud certifications (AWS, Azure, or Databricks)

Technical Skills:

• Databricks (Lakehouse architecture, notebooks, jobs, Unity Catalog)
• Spark / PySpark
• SQL (advanced querying and optimization)