Lead the data engineering team by driving the design, build, test, and launch of new data pipelines and models on the production data platform
Define and implement the processes needed to achieve operational excellence in all areas, including agile development, and data solutions
Oversee the design, development and maintenance of data infrastructure, including data warehouses, data lakes, data integration components supporting optimal extraction, transformation, and loading of data
Leading the communication with vendors, clients, the leadership team, and other stakeholders to facilitate effective project management and provide ongoing support
Collaborate with business owners for roadmap planning and prioritization, to deliver robust cloud-based data solutions for our customers
Work closely with cross-functional teams to propel and execute necessary solution enhancements and provide support for existing solutions
Define and enforce best practices for data engineering, data modeling, and data quality to ensure accuracy, reliability, reusability, security and consistency of data
Evaluate and implement new technologies, tools, and frameworks to improve the performance and efficiency of the data engineering team
Lead the planning, prioritization, and execution and tracking of data engineering projects, ensuring timely delivery and alignment with business objectives
Establish and monitor key performance indicators (KPIs) to measure the performance and effectiveness of the data engineering team
Stay current with industry trends and advancements in data engineering, Azure Cloud, and provide strategic guidance for adopting new technologies or methodologies
Qualifications
8-12 years of demonstrable experience in data engineering, analytics, data warehousing, data management and data governance
Experience managing data engineers and guiding a team of engineers through project planning, execution, tracking and monitoring, and quality control stages
Solid experience with cloud-based data tools and platforms
Proficient in implementing efficient cost management strategies, particularly about storage and computational expenses.
Experience building processes supporting data transformation, data structures, metadata, security, governance, and workload management.
Experience supporting and working with cross-functional teams in a dynamic environment
Expertise in designing and optimizing data models, RDBMS, NoSQL DBs, and data warehousing solutions
Excellent leadership, communication, and interpersonal skills, with the ability to effectively collaborate with cross-functional teams
Proven ability to drive innovation, make strategic decisions and lead complex data engineering initiatives to successful completion
Skill Required
Must Haves:
SQL, Python, PySpark, Spark SQL, Spark, Distributed Systems
Databricks, ADLS Gen 2, Azure DevOps, Azure Data Factory
ETL, Building Data Pipelines, Data Warehousing, Data Modelling and Governance
Agile Practices, SDLC, DevOps practices, and CI/CD pipelines for data engineering workflows
Solid understanding of the Microsoft Azure stack for large-scale data engineering developments and deployments is highly preferred
Hands-on experience with Azure Databricks, including data ingestion, data transformation, workflow management and optimization, monitoring and troubleshooting Spark Jobs
Ability to set up and manage Azure Data Lake Storage (ADLS) Gen 2 accounts, and familiarity with data lake architecture and best practices
Knowledge of Azure Key Vaults for securely storing and managing cryptographic keys, secrets, and certificates
Good To Have
Event Hubs for log management
Workflow Orchestration
Cosmos DB
Power BI
Professional Services Background
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