Data Tech Lead

3 days ago

Bengaluru, Karnataka, India Sonata Software Full time

About Sonata Software

In today's market, we observe a distinct duality in technology adoption. On one front, clients are keenly focused on cost containment, while on the other, there is a strong drive to modernize their digital storefronts, aiming to appeal to both consumers and B2B customers alike.

As a leading Modernization Engineering company, we aim to deliver modernization-driven hypergrowth for our clients based on the deep differentiation we have created in Modernization Engineering, powered by our Lightening suite and 16-step Platformation playbook. In addition, we bring agility and systems thinking to accelerate time to market for our clients.

Headquartered in Bengaluru, India, Sonata Software has a strong global presence, with strategic operations spanning across key regions such as the US, UK, Europe, APAC, and ANZ. We are a trusted partner of world-leading companies in TMT (Telecom, Media, and Technology), Retail & CPG, Manufacturing, BFSI (Banking, Financial Services and Insurance), and HLS (Healthcare and Lifesciences). Our bouquet of Modernization Engineering services cuts across Cloud, Data, Dynamics, Contact Centers, and around newer technologies like Generative AI, MS Fabric, and other modernization platforms.

To know more, visit: www.sonata-software.com

If this opportunity aligns with your aspirations, please share your updated profile with Shobha at (mv.shobha@sonata-software.com)

Job Description for Data Tech Lead

Role: Data Tech Lead

Location : Bangalore/Hyderabad

Years of Experience: 10+ Years

Role Description:

The Data Tech Lead will be responsible for leading the design, development, and optimization of data pipelines across various cloud platforms such as Azure, MS Fabric, AWS, and Snowflake. This role will focus on architecting scalable ETL solutions, ensuring robust data governance practices, and optimizing the overall performance of data systems. The Data Tech Lead will collaborate with cross-functional teams to build and maintain cloud-based data platforms and support enterprise-level analytics and machine learning initiatives. As a technical leader, the individual will be expected to drive best practices, mentor junior engineers, and help shape the strategic direction of data engineering practices within the organization.

Key Responsibilities:

  • Lead the design and implementation of data pipelines on Azure, AWS, and Snowflake, ensuring scalability, reliability, and performance.
  • Architect and optimize scalable ETL/ELT solutions using tools such as ADF, AWS Glue, or dbt.
  • Ensure data governance best practices, including managing role-based access, data encryption, data lineage, and compliance with relevant regulations.
  • Design and implement data models that are optimized for analytics and reporting, ensuring efficiency and performance across large data sets.
  • Collaborate with data scientists, analysts, and other technical teams to support enterprise analytics and machine learning projects.
  • Mentor and guide junior engineers, fostering a collaborative and high-performing team culture.
  • Drive the adoption of cloud-based data technologies and best practices across the organization.
  • Stay up to date with emerging technologies and trends in data engineering, and incorporate new tools and techniques where appropriate.

Required Skills:

  • Cloud Data Engineering (Azure, AWS): Significant experience in designing, implementing, and optimizing data pipelines on cloud platforms such as Azure and AWS. A deep understanding of the cloud infrastructure and data services offered by these platforms is essential.
  • MS Fabric & Synapse Analytics: In-depth knowledge of Microsoft Fabric, Synapse pipelines, and Lakehouse architecture for building and managing scalable data platforms in the cloud.
  • Snowflake Data Engineering: Expertise in Snowflake architecture, including performance tuning, data sharing, and efficient management of large-scale data.
  • ETL & Data Pipeline Development: Advanced skills in designing and implementing scalable ETL/ELT processes using tools such as Azure Data Factory (ADF), AWS Glue, or dbt. Experience in handling large volumes of data in complex pipelines is required.
  • Data Modeling (Star/Snowflake Schema): Ability to design optimized data models for efficient analytics and reporting, particularly star and snowflake schemas. This includes ensuring that the models are scalable and performant.
  • SQL & Performance Optimization: Strong expertise in SQL, including advanced techniques such as indexing, partitioning, and query performance tuning. The ability to optimize complex queries and improve performance is essential.
  • Big Data & Lakehouse Architecture: Experience working with cloud-based data lakes, including Delta Lake, Parquet, and other big data technologies. A solid understanding of the Lakehouse architecture and how to build and manage data lakes is required.
  • Streaming & Real-time Processing: Knowledge of real-time data processing using tools like Kafka, Kinesis, or Azure Event Hubs. Ability to design systems that handle streaming data for immediate analytics.
  • Data Governance & Security: Expertise in implementing role-based access control, data encryption, data lineage, and ensuring compliance with industry regulations (such as GDPR, HIPAA, etc.) in a cloud data environment.
  • CI/CD & DevOps for Data: Experience in using Git, Terraform, Azure DevOps, or AWS CodePipeline for automating the deployment and integration of data solutions. Understanding of DevOps practices applied to data engineering is important.
  • Team Leadership & Mentoring: Proven ability to lead teams, set best practices, and mentor junior engineers. Strong leadership skills and the ability to foster a collaborative and innovative work environment.

Soft Skills:

  • Agile Mindset & Team Player: Must be able to work in an agile environment, effectively collaborating with cross-functional teams and embracing iterative development practices.
  • Communication Skills: Excellent verbal and written communication skills are required to interact with stakeholders at all levels, explain complex technical concepts, and lead discussions on data engineering strategies.

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