Lead data engineering teams to design, build, and maintain scalable cloud-native data pipelines, data platforms, and data products using Snowflake, Databricks, Airflow, and modern architectures. Optimize ETL/ELT, SQL, and transformations for analytics, reporting, and AI use cases; ensure data quality, governance, observability, and stakeholder alignment. Produce documentation, runbooks, and present technical solutions to business and technical audiences.
As a technical lead, you will guide engineering teams, establish best practices, and ensure successful delivery across multiple workstreams while enabling data-driven decision-making across the enterprise.
Data Engineering & Platform Development
- Design, develop, and maintain scalable data pipelines and data products supporting analytics, reporting, AI, and operational use cases.
- Build and optimize ETL/ELT frameworks for large-scale data ingestion, transformation, validation, and consumption.
- Develop and manage cloud-native data platforms leveraging Snowflake, AWS, Apache Airflow and modern data architectures.
- Create scalable data models, data marts, semantic layers, and curated datasets that support enterprise analytics initiatives.
- Optimize SQL workloads, transformation logic, and query performance to improve scalability and cost efficiency.
- Establish reusable engineering frameworks, accelerators, and best practices to improve delivery consistency across projects.
- Ensure high standards of data quality, reliability, governance, and observability throughout the data lifecycle.
- Develop and maintain Snowflake-based data ecosystems, leveraging advanced features for performance optimization and data sharing.
- Build and orchestrate data workflows using Airflow and other workflow scheduling platforms.
- Collaborate directly with client stakeholders to gather requirements, define roadmaps, and develop scalable technical solutions.
- Present solution designs, technical recommendations, and project updates to both technical and business audiences.
- Prepare and maintain comprehensive project documentation, technical specifications, architecture diagrams, and operational runbooks.
Required Qualifications
- 4+ years of experience in Data Engineering, Big Data Engineering, or Cloud Data Platform development.
- Bachelor's or Master's degree in Computer Science, Engineering, Analytics, Mathematics, Information Systems, or related disciplines.
- Strong hands-on expertise in SQL, Python, and PySpark.
- Extensive experience working with Snowflake, Databricks, or similar cloud-native data platforms.
- Proven experience building and supporting large-scale ETL/ELT data pipelines.
- Strong understanding of data warehousing concepts, dimensional modeling, and modern Lakehouse architectures.
- Experience implementing Medallion Architecture and enterprise-grade data modeling practices.
- Hands-on experience with workflow orchestration tools such as Apache Airflow or equivalent scheduling frameworks.
- Experience working with cloud ecosystems including AWS, Azure, or GCP.
- Strong knowledge of performance tuning, optimization, monitoring, and operational support for data platforms.
- Demonstrated experience leading engineering teams and coordinating with client and internal stakeholders.
- Excellent analytical, problem-solving, communication, and stakeholder management skills.
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Ability to work independently and lead complex initiatives in fast-paced consulting environments.
Preferred Qualifications
- Experience with streaming and real-time data processing frameworks.
- Familiarity with DataOps, CI/CD, Infrastructure as Code, and DevOps practices.
- Experience with data governance, data quality frameworks, and metadata management.
- Exposure to AI/ML data pipelines and feature engineering workflows.
- Experience with visualization tools such as Tableau, Power BI, or Looker.
- Hands-on experience with Big Data technologies including Spark, Hadoop, Hive, HBase, Kafka, or related platforms.
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Consulting or client-facing delivery experience in enterprise-scale environments.
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