Data Engineer (3-6 yrs)

Job Summary :

The Data Engineer will play a pivotal role in building scalable, secure, and cost-effective data solutions to support innovation and business growth. This position involves collaborating with stakeholders, such as Business Partners, Product Owners, and Data Science teams, to design and implement data products, pipelines, and processes that empower data-driven decision-making. The role requires leveraging best-in-class engineering practices to deliver robust data products within a DevSecOps framework.

Must-have skills and tools :

- 3+ years of progressive experience with full-stack data frameworks (ETL/ELT, data analysis, compute & storage, pipelines, orchestration).

- 2+ years of hands-on experience in Cloud Architecture (Azure) and cloud-based databases (Synapse, Databricks).

- Proficiency in SQL/PySpark, Python, DBT, and other ETL platforms (e.g., IICS, SAP BODS, SAP Datasphere).

- 2+ years implementing data pipelines using data mesh/fabric concepts.

- 3+ years of experience in Agile methodology (Scrum/Kanban) within a DevSecOps model.

- 2+ years leading data engineering teams in Consumer/Healthcare Goods industries with an excellent understanding of business domains.

- Ability to build data pipelines addressing granularity, data gaps, harmonization, and normalization of structured and unstructured data.

- Strong interpersonal and communication skills for cross-functional collaboration.

Qualifications : Undergraduate degree in Technology, Computer Science, applied data sciences or other related fields; advanced degree in related fields preferred.

Good to have :

- Demonstrated expertise in cloud-based data integration techniques (API, stream, file).

- Track record of contributing to high-profile projects with demanding deadlines.

- Ability to prioritise work items and estimate required effort effectively.

Key Responsibilities :

Data Solution Design : Partner with architecture and platform teams to build innovative data product capabilities that align with business growth strategies.

Development & Delivery : Develop highly reliable and secure data pipelines while ensuring scalability and performance.

Stakeholder Collaboration : Work closely with Business Analytics leaders to align data products with business needs and articulate value through data and technology.

Framework Implementation : Prioritize and implement data engineering methodologies to optimize platform and solution performance.

Team Participation : Act as an integral part of the data engineering team within the DevSecOps framework.

Thought Leadership : Serve as a thought leader in data technology and empower businesses through robust data solutions.

Trusted Partnerships : Collaborate with Data Engineering, Architecture, and Science teams to scale and adopt data products effectively.