Manager of Data Analytics & Data Science

Job Overview

TE Connectivity is a global technology leader that designs and manufactures connectivity and sensor solutions, serving industries that are transforming the future.  The Channel Business Unit (CBU) manages TE’s global distribution network and plays a critical role in driving growth, enhancing customer experience, and enabling commercial excellence across 300+ distributor partners worldwide. Channel BU brings together Sales Enablement, Marketing, Pricing, Customer Service, Digital, and PMO functions to accelerate revenue, deliver digitally enabled experiences, and achieve above-market growth.

Role Definition

We are seeking an experienced and hands-on Data Analytics & Data Science Delivery Manager to lead and grow our India-based analytics team supporting the Channel Business Unit. This role is accountable for end-to-end delivery across three critical pillars: Channel Data Management, Analytics Products (dashboards, self-service BI, reporting), and AI / Data Science Products (predictive models, NLP, machine learning solutions).

The ideal candidate combines strong people leadership with deep technical fluency, someone who can manage a team of 8–12 BI developers, analytics engineers, and data scientists while also rolling up their sleeves to architect solutions, shape demand with business stakeholders, and drive adoption of enterprise data platforms. This role operates at the intersection of business strategy and technical execution, ensuring the Channel BU leverages data as a strategic asset for growth, efficiency, and innovation.

Based in Bangalore, India, this role will partner closely with the NA based Data & Analytics product owner, Channel BU leadership, global DIA teams, and cross-functional stakeholders across AMER, EMEA, and APAC regions.

Tasks and responsibilities

What your background should look like

• Bachelor's degree in Engineering (BE/BTech), Computer Science, Statistics, Mathematics, Economics, or related quantitative field. Master's degree preferred.
• 10+ years of progressive experience in data analytics, business intelligence, data engineering, or data science roles.
• 3+ years of direct people management experience leading teams of 5+ resources, with a proven track record of building and developing high-performing analytics teams.
• 5+ years of hands-on experience with BI tools (Power BI, Tableau) including dashboard design, semantic data modeling, and performance optimization.
• 3+ years of experience with cloud data platforms (AWS Redshift, S3, Databricks, Azure) and modern data warehouse architectures.
• 2+ years of experience with Python, R, or similar languages for data analysis, automation, and/or machine learning.
• Demonstrated experience delivering AI/ML or data science projects from ideation through production deployment.
• Strong SQL skills and deep understanding of data warehousing concepts (star/snowflake schemas, ETL/ELT, incremental loading).

Ideal Soft Skills

• Business Acumen: Ability to translate business needs into data-driven strategies and deliver measurable impact. Experience in Channel/Distribution business models is a strong plus.
• Delivery Management: Proven ability to manage multiple concurrent workstreams, balance competing priorities, and deliver on committed timelines using Agile methodologies.
• Leadership & Influence: Strong ability to engage, communicate, and influence senior executives and cross-functional teams across geographies and time zones.
• Technical Depth: Hands-on fluency with modern data stack — data engineering, analytics, and data science — sufficient to guide architecture decisions, conduct code reviews, and unblock the team.
• Change Management: Experience driving organizational change, legacy system modernization, platform migration, and data literacy initiatives.
• Communication: Exceptional written and verbal communication skills; ability to present complex analytical findings to executive audiences through data storytelling.

Technical background

• Data Platforms: Databricks, AWS S3, Azure, Snowflake with deep understanding of star/snowflake schemas, fact/dimension tables, and data warehousing concepts
• ETL/ELT & Orchestration: Azure Data Factory, AWS Glue, dbt, Apache Airflow, or similar pipeline orchestration tools
• Data Governance: Data lineage, cataloging, quality frameworks, and metadata management
• BI Tools: Power BI (DAX, Power Query, semantic models, row-level security) and Tableau (LOD expressions, Tableau Prep, Tableau Server/Cloud)
• Data Modeling: Semantic model design, star/snowflake schema optimization, incremental refresh strategies
• ML Platforms: Databricks MLflow, AWS SageMaker, Azure ML, or similar
• Generative AI: Familiarity with Microsoft Copilot, Databricks AI/Genie, Salesforce Einstein, LLM-based solutions, and prompt engineering

Competencies