Data Engineer - Python/SQL (5-8 yrs)

Role : Data Engineer ( Pune,Hybrid and Has to go to office thrice in a month)

Total Experience : 6 - 9 yrs Relevant Experience: Minimum 5+ yrs

Preferred candidates Pune based Hinjewadi, Kharadi, Baner, Viman Nagar locations and has to go to office thrice in a month

Preferred domain : Private Equity / Venture Capital and associated secondary market

Location : Pune | Hybrid ( Thrice in a month)

Work timings : Initial: Start time 11 AM (IST) -> 5 hours + Start time 8 / 8:30 PM (IST) -> 3 hours

Skills & Experience :

- At least 5+ years of experience as a Data Engineer

- Hands-on and in-depth experience with Star / Snowflake schema design, data modeling, data pipelining and MLOps.

- Experience in Data Warehouse technologies (e.g. Snowflake, AWS Redshift, etc)

- Experience in AWS data pipelines (Lambda, AWS glue, Step functions, etc)

- Proficient in SQL

- At least one major programming language (Python / Java)

- Experience with Data Analysis Tools such as Looker or Tableau

- Experience with Pandas, Numpy, Scikit-learn, and Jupyter notebooks preferred

- Familiarity with Git, GitHub, and JIRA.

- Ability to locate & resolve data quality issues

- Ability to demonstrate end to ed data platform support experience

Other Skills :

- Individual contributor

- Hands-on with the programming

- Strong analytical and problem solving skills with meticulous attention to detail

- A positive mindset and can-do attitude

- To be a great team player

- To have an eye for detail

- Looking for opportunities to simplify, automate tasks, and build reusable components.

- Ability to judge suitability of new technologies for solving business problems

- Build strong relationships with analysts, business, and engineering stakeholders

- Task Prioritization

- Familiar with agile methodologies.

- Fintech or Financial services industry experience

- Eagerness to learn, about the Private Equity/Venture Capital ecosystem and associated secondary market

Responsibilities :

- Design, develop and maintain a data platform that is accurate, secure, available, and fast.

- Engineer efficient, adaptable, and scalable data pipelines to process data.

- Integrate and maintain a variety of data sources: different databases, APIs, SAASs, files, logs, events, etc.

- Create standardized datasets to service a wide variety of use cases.

- Develop subject-matter expertise in tables, systems, and processes.

- Partner with product and engineering to ensure product changes integrate well with the data platform.

- Partner with diverse stakeholder teams, understand their challenges and empower them with data solutions to meet their goals.

- Perform data quality on data sources and automate and maintain a quality control capability.