Key Responsibilities

1. Data Platform Engineering

• Design, build, and maintain scalable ETL/ELT pipelines.
• Develop and optimize analytical data models and data warehouse structures.
• Integrate data from operational databases, third-party systems, APIs, and streaming platforms.
• Continuously improve scalability, performance, and maintainability of the data platform.

2. Data Reliability & Quality

• Implement automated data validation and quality assurance processes.
• Monitor pipeline health, data freshness, and platform reliability.
• Design monitoring and alerting mechanisms for data infrastructure.
• Troubleshoot data issues and ensure timely incident resolution.

3. Analytics Enablement

• Collaborate closely with Data Scientists, Analysts, and business stakeholders to deliver trusted analytical datasets.
• Build reusable datasets and feature-ready data models that support reporting, experimentation, forecasting, and machine learning initiatives.
• Improve data accessibility through well-designed data models, documentation, and metadata.

4. Data Governance

• Contribute to data governance initiatives, including metadata management, data lineage, ownership, and documentation.
• Promote best practices for data consistency, security, and accessibility.
• Help establish standards for analytical data assets across the organization.

5. Platform Automation & DevOps

• Automate data workflows and orchestration processes.
• Build and maintain CI/CD pipelines for data engineering projects.
• Containerize services where appropriate and improve deployment automation.
• Participate in the evolution of the company's data architecture and engineering standards.

Required Competencies & Qualifications

Core Competencies

• Data Architecture & Modeling: Ability to design scalable data models and analytical data platforms.
• Data Pipeline Engineering: Strong experience building reliable, maintainable, and scalable batch and streaming data pipelines.
• Data Integration: Experience integrating heterogeneous data sources using ETL/ELT, APIs, CDC, and messaging systems.
• Data Quality & Observability: Strong understanding of data validation, monitoring, alerting, and data reliability practices.
• Performance Optimization: Ability to optimize SQL queries, indexing strategies, storage design, and large-scale data processing workloads.
• Workflow Orchestration & Automation: Experience building production-grade automated workflows and orchestration pipelines.
• Data Governance: Understanding of metadata management, data lineage, documentation, access control, and governance principles.
• Production Mindset: Experience building secure, scalable, production-ready data platforms with strong operational ownership.
• Cross-functional Collaboration: Ability to translate business requirements into reliable technical solutions while working closely with Product, Engineering, Analytics, and Data Science teams.

Technical Skills

Required

• Advanced SQL
• Python
• ETL / ELT Development
• Data Modeling
• Data Warehouse Design
• Apache Airflow (or equivalent orchestration tools)
• Relational databases
• ClickHouse or other analytical databases
• Git
• REST API Integration
• Apache Spark / PySpark
• Kafka or other streaming platforms
• Docker
• CI/CD
• Prometheus & Grafana

Experience & Education

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
• 3+ years of professional experience in Data Engineering or Analytics Engineering.
• Proven experience designing and maintaining production-grade data platforms.
• Experience collaborating with analytics, business intelligence, or machine learning teams.
• Experience in Insurance, Financial Services, E-commerce, or other data-intensive industries is a plus.