15+ years of overall experience with 8+ years in leading large-scale data engineering or data platform initiatives.
Define the strategic direction and roadmap for the Data Engineering practice.
Establish best practices, frameworks, and standards across data engineering projects.
Lead innovation and solution development aligned with emerging technologies and market trends.
Represent the practice in client meetings, proposals, and presentations.
Engage with clients to understand their business needs and define data strategies.
Support sales and pre-sales teams with solutioning, estimation, and proposal development.
Should be abreast of industry trends and emerging technologies in cloud platforms, real-time streaming, and data ops as well as data for AI.
Lead the evaluation and adoption of modern data engineering tools and platforms.
Drive internal PoCs, accelerators, and reusable IP development.
Build, mentor, and lead a high-performing team of data engineers, architects, and technical leads.
Oversee delivery of complex data engineering projects, ensuring quality, timeliness, and customer satisfaction.
Define scalable delivery models and reusable components to improve efficiency and reduce time-to-market.
Architect and oversee large-scale, automated data pipelines using tools like Apache Spark, Kafka, Airflow.
Unified data management across on-premises and cloud.
Serverless Data Processing (AWS Lambda, Azure Functions).
Real time streaming Apache Flink, Kafka, AWS Kinesis.
Low Code Data Pipeline AWS Glue, Airbyte.
ETL & messaging tools: Spark, Kafka, Airflow.
Hands on experience on SQL, NoSQL, data lakes, warehouses. Knowledge on Snowflake is a big plus.
Strong hands-on knowledge of SQL, Python/Scala, and distributed data architectures.
Cloud platforms: AWS, GCP, Azure (incl. IaC, DevOps).
Solid understanding of data governance, data security, and compliance frameworks.
Collaborate with clients to understand their business needs and translate them into technical solutions that drive value.

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