Senior Data Engineer

BU / Function Description :

Corporate Finance at TE Connectivity is the strategic backbone of the enterprise, partnering with FP&A, Controllership, Treasury, Tax, and Operations to deliver insights that drive global decisions. The FP&A Technology / Data & BI Office modernizes the finance data ecosystem by building declarative pipelines, governed data products on Databricks (OneData), building Transformations using DBT Tool, semantic models on Microsoft Fabric, and AI applications (Genie Spaces, LangChain / LangGraph agents) at enterprise scale.

Responsibilities :

- Build declarative pipelines (Databricks Lakeflow / DLT) on Medallion Architecture (Bronze - Silver - Gold), delivering governed, audit-ready data products with Unity Catalog lineage, access controls, and CI/CD (Asset Bundles, GitHub Actions).

- Decode SAP ABAP programs (Z-reports, Transaction codes) into modern Databricks pipelines and orchestrate pipelines via Databricks Workflows.

- Build production grade, reliable ETL/ELT processes using Python / PySpark transformations notebooks in Databricks / DBT Tool to transform, validate and publish data from multiple sources or bronze layers.

- Design optimized, scalable and well documented Gold Layers and semantic models (Direct Lake, star-schema, RLS, incremental refresh) for high-performance finance reporting in Microsoft Fabric / Sigma Computing / Databricks.

- Build AI Integrated Insights on curated Gold-layer finance datasets to enable natural-language analytics for Finance Users.

- Act as strategic consultants to senior finance stakeholders like elicit requirements, frame problems, build business cases, and produce BRDs, dependency matrix and required documentation.

- Establish architecture standards, perform RCA on data and platform issues, close lineage / KPI gaps, and drive structural fixes.

- Mentor engineers / BI analysts and Finance users on copilots and promoting best practices.

Competencies & Experience :

- Bachelor's / Master's in Computer Science, AI Engineering, Data Science; Finance domain experience is a plus.

- 6 - 8 years in Data Engineering and Data Modelling.

- SAP system knowledge and ABAP program conversion to SQL will be an added advantage.

- 5+ Years of Hands-On Databricks (Unity Catalog, Delta, DLT / Lakeflow, Workflows, Genie, Model Serving, Vector Search) and Transformations on DBT tool using DBT.

- Deep knowledge of Medallion Architecture, Lakehouse / data mesh design, and enterprise data governance (Unity Catalog, Purview, RLS / CLS).

- Hands-on experience with cloud native platforms especially with AWS (S3, Redshift, Bedrock, Lambda, SageMaker), Oracle, and Hyperion.

- Proficiency in SQL, SAP Transactions understanding, Python, and PySpark is must.

- Strong knowledge of Data modelling, validation techniques and troubleshooting issues.

- Working knowledge of CI/CD pipelines, Git versioning, data ingestion and transformations, and big data architecture.

- Strategic mindset and strong consulting skills like elicit requirements, leading workshops, frame business cases, and presenting senior leadership with experience in RCA, GAP Analysis and Risk Assessment.

- Proven track record automating manual finance processes at enterprise scale.

We Value :

- Databricks Certified DE (Associate or Professional).

- AWS Certification (Associate level or higher).

- Finance Process Experience.

- SAP Familiarity with Basic ABAP Programming experience.

- DBT Tool Certified will be an advantage.

Motivational/Cultural Fit :

- Demonstrate Customer Focus

- Communicate Effectively

- Collaboration for Achievement

- Act with an Entrepreneurial Drive

- Results driven

Skills

Databricks, Python, PySpark, SQL, Data Engineering, Talend, AWS, Data Modeling, ETL