Technical Lead

Description :
Notice Period Requirement : 0-30 days

About The Role

• As a Data Platform Lead, you will utilize your strong technical background and hands-on development skills to design, develop, and maintain data platforms.
• Leading a team of skilled data engineers, you will create scalable and robust data solutions that enhance business intelligence and decision-making.
• You will ensure the reliability, efficiency, and scalability of data systems while mentoring your team to achieve excellence.
• Collaborating closely with our clients CXO-level stakeholders, you will oversee pre-sales activities, solution architecture, and project execution.
• Your ability to stay ahead of industry trends and integrate the latest technologies will be crucial in maintaining our competitive edge.

Key Responsibilities

• Client-Centric Approach : Understand client requirements deeply and translate them into robust technical specifications, ensuring solutions meet their business needs.
• Architect for Success : Design scalable, reliable, and high-performance systems that exceed client expectations and drive business success.
• Lead with Innovation : Provide technical guidance, support, and mentorship to the development team, driving the adoption of cutting-edge technologies and best practices.
• Champion Best Practices : Ensure excellence in software development and IT service delivery, constantly assessing and evaluating new technologies, tools, and platforms for project suitability.
• Be the Go-To Expert : Serve as the primary point of contact for clients throughout the project lifecycle, ensuring clear communication and high levels of satisfaction.
• Build Strong Relationships : Cultivate and manage relationships with CxO/VP level stakeholders, positioning yourself as a trusted advisor.
• Deliver Excellence : Manage end-to-end delivery of multiple projects, ensuring timely and high-quality outcomes that align with business goals.
• Report with Clarity : Prepare and present regular project status reports to stakeholders, ensuring transparency and alignment.
• Collaborate Seamlessly : Coordinate with cross-functional teams to ensure smooth and efficient project execution, breaking down silos and fostering collaboration.
• Grow the Team : Provide timely and constructive feedback to support the professional growth of team members, creating a high-performance culture.

Qualifications

• Masters (M. Tech., M.) in Computer Science or equivalent from reputed institutes like IIT, NIT preferred
• Overall 6-8 years of experience with minimum 2 years of relevant experience and a strong technical background.
• Experience working in mid size IT Services company is preferred

Preferred Certification (one Or More)

• AWS Certified Data Analytics Specialty
• AWS Solution Architect Professional
• Azure Data Engineer + Solution Architect
• Databricks Certified Data Engineer / ML Professional

Technical Expertise

• Advanced knowledge of distributed architectures and data modeling practices.
• Extensive experience with Data Lakehouse systems like Databricks and data warehousing solutions such as Redshift and Snowflake.
• Hands-on experience with data technologies such as Apache Spark, SQL, Airflow, Kafka,
• Jenkins, Hadoop, Flink, Hive, Pig, HBase, Presto, and Cassandra.
• Knowledge in BI tools including PowerBi, Tableau, Quicksight and open source equivalent like Superset and Metabase is good to have
• Strong knowledge of data storage formats including Iceberg, Hudi, and Delta.
• Proficient programming skills in Python, Scala, Go, or Java.
• Ability to architect end-to-end solutions from data ingestion to insights, including designing data integrations using ETL and other data integration patterns.
• Experience working with multi-cloud environments, particularly AWS and Azure.
• Excellent teamwork and communication skills, with the ability to thrive in a fast-paced, agile environment.

Primary Skills : (must have)
Data Platform Architecture & Engineering

• End-to-end data platform architecture (ingestion ? processing ? storage ? analytics)
• Design of scalable, reliable, high-performance distributed data systems
• Strong data modeling and schema design for analytical and operational workloads

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