Data Engineer - ETL (3-12 yrs)

Role : Data Engineer (2 positions - Mid level & Senior level)

Full-time positions with D Square Consulting Services Pvt Ltd

Required Experience : 3 to 12 years

Location : Bangalore

The candidate who can join within 30 days may only apply

Mode : Work from office

We have 2 openings one is for Mid-level & the other is for Senior-level (Sr. Platform Engineer) positions.

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, GCP, AWS) and cloud-based databases (Synapse, Databricks, Snowflake, Redshift).

- 3+ years of experience in developing and maintaining SQL/PySpark, Python, DBT, and other ETL platforms (e.g., IICS, SAP BODS, SAP Datasphere). [for Mid-level & Senior level]

- Platforms enabling data streaming (Kafka), microservices, and data integration. [Only for Senior level]

- 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 prioritize 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.

- Provide leadership for data platforms, collaborating with architecture teams to build next-gen capabilities.

- Conduct proofs of concept and deliver secure, scalable, cost-effective platforms.

- Stay updated with emerging technologies and mentor platform engineers.

- Ensure high-quality, reliable data platforms aimed at maximizing business value.

- Lead data platform operations within a DevSecOps framework using modern technology stacks.

- Partner with Business Analytics leaders to align platform development with business needs.

- Drive prioritization and selection of tools and technologies for optimal scalability, flexibility, and performance.

- Forge partnerships with Data Engineering, Architecture, and Data Science teams to scale data products.