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Acuity Analytics
Senior Data Engineer - Delivery Lead
Acuity Analytics
Pune•On-site•7-10 Yrs•2 days ago
Job description
Overview
Seeking a highly skilled Senior Data Engineer to design, build, and maintain scalable data platforms, ETL processes, and AI-enabled data solutions.
Responsibilities
- Design, develop, and maintain scalable ETL workflows and data pipelines that support business-critical applications and analytics.
- Build and manage data orchestration workflows using Apache Airflow.
- Develop high-quality, production-ready solutions using Python.
- Implement and maintain CI/CD pipelines leveraging GitHub Actions.
- Manage source control, branching strategies, and code reviews through GitHub repositories.
- Deploy, monitor, and optimize applications running on Azure Kubernetes Service (AKS).
- Develop and maintain containerized solutions using Docker.
- Integrate and support Generative AI solutions utilizing OpenAI and Anthropic Claude models.
- Collaborate with data scientists, ML engineers, and business stakeholders to deliver reliable data products.
- Ensure data quality, monitoring, observability, security, and governance standards are met.
- Troubleshoot production issues and drive continuous improvement across data engineering platforms.
- Document architecture, processes, and best practices for enterprise data solutions.
- Bachelor's or master's degree in computer science, Engineering, Information Technology, or a related field.
- 7–10 years of experience in data engineering, software engineering, or related technical disciplines.
- Strong hands-on experience with:
- Python
- GitHub and Git-based development workflows
- GitHub Actions CI/CD
- Azure Kubernetes Service (AKS)
- Apache Airflow
- Docker
- Building and maintaining enterprise-scale ETL processes and data pipelines
Qualifications
- Strong understanding of software engineering best practices, testing frameworks, and deployment automation.
- Excellent problem-solving, debugging, and performance optimization skills.
- Strong communication and stakeholder management capabilities.
- Familiarity with data governance, data quality frameworks, and monitoring tools.
- Exposure to machine learning and AI/ML operations (MLOps) environments.
- Data Engineering: ETL, Data Pipelines, Data Integration, Data Orchestration, Data Quality
- Cloud & DevOps: Azure Kubernetes Service (AKS), Docker, GitHub, GitHub Actions, CI/CD
- Programming: Python
- Workflow Orchestration: Apache Airflow
- AI & Generative AI: OpenAI, Claude, LLM Integrations
- Preferred Data Platforms: Snowflake, S&P Xpressfeed