Data Engineer Support

Ovii's Interpretation of the Role

Flentas seeks a senior Data Engineer to design, build and optimise scalable batch and real‑time data pipelines on AWS. The role combines hands‑on development with architecture discussions, serving enterprise customers across industries.

Role Snapshot

• Design and implement scalable data pipelines
• Develop batch and streaming ETL/ELT workflows
• Build and maintain Data Lakes, Lakehouses and Warehouses
• Optimise Spark jobs, SQL queries and cloud resource usage
• Collaborate with data scientists, analysts and architects
• Ensure data quality, security and governance

Must-Have Requirements

• Python
• PySpark / Apache Spark
• Databricks & Delta Lake
• AWS Cloud Platform
• SQL
• Apache Airflow (or equivalent)
• Git
• Data Engineering
• Big Data
• Cloud Data Platforms

Work Setup

• Location: Pune, India
• Work mode: ONSITE
• Employment type: Full-Time

Not Specified in JD

• Visa sponsorship
• Salary range
• Remote eligibility
• Education requirement
• Certifications
• Relocation
• Notice period
• Travel
• Security clearance
• Coding test
• Portfolio
• GitHub
• Writing sample
• Cover letter

What You'll Likely Work On

• Design, develop and maintain batch and real‑time pipelines using Python, PySpark and Databricks
• Create ingestion frameworks for structured, semi‑structured and unstructured data sources
• Implement and manage ETL/ELT processes for large‑scale analytics and reporting
• Architect and sustain Data Lakes, Lakehouse and Data Warehouse solutions
• Collaborate with cross‑functional stakeholders to translate business needs into technical designs
• Apply data quality, security and governance standards across platforms
• Tune Spark jobs, SQL queries and cloud resources for performance and cost efficiency
• Set up monitoring, logging and alerting for pipeline health

Good Fit If You Have

• Experience with cloud‑native data platforms (AWS)
• Familiarity with real‑time streaming data processing
• Exposure to generative AI or advanced analytics use cases
• Comfort handling diverse data formats (structured, semi‑structured, unstructured)
• Ability to contribute to architecture and code‑review discussions