Job
Description
ql-editor "> Tezo is a new generation Digital AI solutions provider, with a history of creating remarkable outcomes for our customers. We bring exceptional experiences using cutting-edge analytics, data proficiency, technology, and digital excellence. Seeking a highly skilled and modern Data Engineering Manager to lead technical teams in architecting and delivering cutting-edge data solutions across multiple cloud platforms. This role requires deep expertise in AWS, Azure, GCP, Snowflake, and Databricks , along with a strong background in data engineering, architecture, and analytics. As a Technical Manager , you will drive end-to-end data solutioning , oversee data pipeline development , and ensure scalability, performance, and security while aligning solutions with business objectives. Key Responsibilities: Solution Architecture: Design and implement modern, scalable, and high-performance data architectures across cloud platforms (AWS, Azure, GCP). Data Engineering Integration: Develop, optimize, and manage ETL/ELT pipelines , data lakes, and real-time streaming solutions using Snowflake, Databricks, and cloud-native tools . Cloud Data Platforms: Deploy and manage data warehousing, analytics, and lakehouse solutions on AWS (Redshift, Glue, S3), Azure (Synapse, ADF, Data Lake), GCP (BigQuery, Dataflow). AI ML Integration: Collaborate with data scientists to integrate AI/ML models into data pipelines and optimize analytics workflows . Data Governance Security: Implement data governance frameworks , compliance (GDPR, CCPA), role-based access controls , and best practices for security across multi-cloud environments. Technical Leadership: Lead and mentor a team of data engineers, define best practices , and drive innovation in data engineering strategies. Performance Optimization: Ensure cost-efficient and high-performance data processing , leveraging Spark, DBT, and cloud-native tools . Cross-Cloud Integration: Design interoperable solutions that leverage multi-cloud capabilities for data movement, transformation, and analytics. Stakeholder Management: Collaborate with business leaders, data analysts, and engineering teams to deliver data-driven solutions aligned with business needs. Required Skills Qualifications: Core Technical Skills: Cloud Data Ecosystems: Hands-on expertise with AWS (Redshift, Glue, S3, Lambda), Azure (Synapse, Data Lake, ADF), GCP (BigQuery, Dataflow, Pub/Sub) . Experience in multi-cloud data strategy and interoperability . Data Engineering Pipelines: Strong experience in ETL/ELT development using Snowflake, Databricks, Apache Spark, DBT, Airflow . Expertise in real-time and batch data processing (Kafka, Spark Streaming, Flink). Databases Warehousing: Strong knowledge of SQL and NoSQL databases (Snowflake, BigQuery, Redshift, DynamoDB, Cosmos DB, MongoDB). Deep experience in Data Lakehouse architecture using Databricks, Delta Lake . Programming Automation: Proficiency in Python, SQL, Scala, Java for data transformations and automation. Experience with IaC (Terraform, CloudFormation, ARM templates) for provisioning cloud data infrastructure. Data Governance Security: Knowledge of RBAC, IAM, encryption, GDPR/CCPA compliance in cloud environments. Experience with data cataloging, lineage, and metadata management . AI ML for Data Pipelines: Hands-on experience integrating AI/ML models into production data pipelines. Leadership Soft Skills: Strong leadership and mentorship experience in managing high-performing engineering teams. Ability to translate business needs into technical requirements and deliver solutions. Excellent stakeholder communication and collaboration skills. Experience in agile methodologies and DevOps for data engineering.
