What are the responsibilities and job description for the Data Analytics Technical Lead position at Allwyn Corporation?
with extensive hands-on experience in Databricks, including advanced data engineering, lakehouse architecture, and performance optimization, supported by relevant Databricks certifications. Proven expertise leading enterprise-scale analytics initiatives across modern cloud and big data platforms.
Well-rounded Background Spanning The Full Analytics Ecosystem, Including
Proven technical leadership in designing and delivering scalable, end-to-end analytics and data engineering solutions leveraging Databricks, Delta Lake, PySpark, SQL, and AWS cloud services including S3, Glue, Lambda, Kinesis, and Redshift. Extensive experience building and optimizing batch, real-time, and streaming ETL/ELT pipelines within modern lakehouse architectures.
Strong expertise in hands on implementation of secure, high-performance, and cost-efficient cloud data platforms with a focus on data quality, governance, lineage, observability, and CI/CD-driven DevOps practices. Hands-on experience with advanced Databricks capabilities including Delta Live Tables (DLT), Unity Catalog, Workflows, and performance tuning of distributed Spark workloads.
Experienced in supporting enterprise AI/ML, analytics, and reporting initiatives through curated and scalable datasets, with deep knowledge of visualization and BI platforms including Power BI and Tableau.
Recognized leader with a track record of mentoring engineering teams, driving delivery excellence, establishing best practices, and collaborating cross-functionally with architects, data scientists, BI teams, security, and business stakeholders to align technical solutions with enterprise data strategy and business objectives.
Holds Databricks Certified Data Engineer Professional certification along with strong expertise across modern analytics ecosystems and cloud-native data platforms.
Well-rounded Background Spanning The Full Analytics Ecosystem, Including
- AI/ML solutions and predictive analytics
- Data warehousing and ETL/ELT modernization
- Business intelligence and visualization tools such as Power BI and Tableau
- Cloud platforms primarily AWS
- Real-time and batch data processing frameworks.
Proven technical leadership in designing and delivering scalable, end-to-end analytics and data engineering solutions leveraging Databricks, Delta Lake, PySpark, SQL, and AWS cloud services including S3, Glue, Lambda, Kinesis, and Redshift. Extensive experience building and optimizing batch, real-time, and streaming ETL/ELT pipelines within modern lakehouse architectures.
Strong expertise in hands on implementation of secure, high-performance, and cost-efficient cloud data platforms with a focus on data quality, governance, lineage, observability, and CI/CD-driven DevOps practices. Hands-on experience with advanced Databricks capabilities including Delta Live Tables (DLT), Unity Catalog, Workflows, and performance tuning of distributed Spark workloads.
Experienced in supporting enterprise AI/ML, analytics, and reporting initiatives through curated and scalable datasets, with deep knowledge of visualization and BI platforms including Power BI and Tableau.
Recognized leader with a track record of mentoring engineering teams, driving delivery excellence, establishing best practices, and collaborating cross-functionally with architects, data scientists, BI teams, security, and business stakeholders to align technical solutions with enterprise data strategy and business objectives.
Holds Databricks Certified Data Engineer Professional certification along with strong expertise across modern analytics ecosystems and cloud-native data platforms.