Senior Data Engineer - BI Data Products & Databricks (Channel)

About TE Connectivity & the Channel BU

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 a skilled and motivated Senior Data Engineer to join our India-based analytics team supporting the Channel Business Unit. This individual contributor role will focus on designing, developing, maintaining, and optimizing trusted curated datasets, reusable data products, semantic/reporting layers, and data pipelines that enable faster and more reliable BI delivery across Channel BU.

The ideal candidate brings strong hands-on experience in Databricks, advanced SQL, Python or PySpark for data transformation, AWS-based data environments, Git/version control, data modeling, and BI-ready data engineering. This role is responsible for building the data foundation that supports executive dashboards, self-service BI, Power BI reporting, Tableau transition needs, commercial analytics, and business performance insights.

This is a hands-on technical role focused on the data engineering side of business intelligence. The Senior Data Engineer will work closely with BI analysts, data product owners, data architects, governance teams, and business stakeholders to ensure that analytics teams have access to accurate, well-modeled, reusable, and high-performing data assets.

The role will primarily support the enterprise Databricks platform on AWS, while also working with or migrating from legacy Amazon Redshift assets where applicable. The successful candidate should be comfortable engineering data pipelines, preparing curated datasets, designing reusable reporting models, improving data quality, and enabling BI teams to deliver dashboards more quickly and consistently.

This role also includes informal leadership responsibilities, including coaching junior engineers or analysts, supporting code reviews, sharing development best practices, and helping establish consistent data engineering patterns for BI delivery

Based in Bangalore, India, this role will report to the India-based Data Analytics & Data Science Delivery Manager and 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.

Design, build, and maintain scalable data pipelines and curated data assets on Databricks to support reporting, analytics, executive dashboards, self-service BI, and commercial performance management.

Develop reliable ETL/ELT processes to ingest, transform, validate, and publish data from multiple sources using advanced SQL, Python, and PySpark.

Leverage Databricks notebooks, Unity Catalog, job scheduling, and performance optimization tools to ensure efficient and reliable data delivery.

Create reusable, maintainable, and well-documented pipelines aligned with enterprise data engineering standards.

Monitor, troubleshoot, and optimize pipeline performance, reliability, and availability of BI-ready datasets.

Build trusted, reusable datasets, data marts, reporting tables, and views for Power BI, Tableau, and other analytics platforms.

Partner with BI teams to translate reporting requirements, KPIs, and business logic into scalable data solutions.

Design business-ready datasets that enable executive reporting, operational analytics, commercial scorecards, and self-service BI.

Create reusable data products that accelerate BI delivery and ensure accurate reporting outcomes.

Design and maintain dimensional models, including fact and dimension tables, star schemas, reporting views, and semantic layers.

Translate business requirements into scalable data structures supporting KPI tracking, trend analysis, drill-down reporting, and self-service analytics.

Collaborate with BI developers to optimize data models for performance, usability, consistency, and maintainability.

Document data structures, business rules, dependencies, assumptions, and refresh processes.

Implement data quality controls, validation routines, reconciliations, and monitoring processes to ensure trusted reporting.

Investigate and resolve data quality issues, pipeline failures, performance bottlenecks, and reporting discrepancies.

Partner with stakeholders to validate outputs and address root causes of data issues.

Support data observability and promote accurate, transparent, and business-ready data assets.

Apply Git and version control best practices to manage code, notebooks, scripts, and reusable assets.

Follow development standards for branching, code reviews, release management, and documentation.

Write clean, modular, and maintainable SQL, Python, and PySpark code.

Contribute to reusable frameworks, templates, and engineering standards that improve quality, consistency, and team efficiency.

Master’s degree in Computer Science, Data Engineering, Data Science, Information Systems, Engineering, Business Analytics, or a related discipline.

Databricks Data Engineer Associate, Databricks Data Engineer Professional, Databricks Lakehouse Fundamentals, or other relevant Databricks certifications.

Relevant AWS data, analytics, or cloud certifications.

Experience working with Amazon Redshift, including legacy asset support, query analysis, data migration, or reporting-layer modernization.

Experience with Databricks Unity Catalog, notebook-based development, job scheduling, and performance optimization.

Experience coaching, mentoring, or nurturing junior data engineers, analysts, or BI developers.

Experience working with commercial, sales, distribution, pricing, marketing, customer care, inventory, POS, or supply chain data.

Familiarity with Power BI data consumption patterns, dashboard performance needs, dataset design, and self-service BI enablement.