Roles and Responsibilities :-

- Own the technical architecture for data analytics and visualization applications, with Python as the primary stack
- Lead, mentor, and grow a team of engineers across maintenance, new feature builds, and analytics initiatives — including hiring input and performance guidance
- Make key technology and design decisions: data pipeline architecture, storage strategy, service boundaries, and tooling choices
- Set and enforce coding standards, review practices, and technical best practices across the team
- Proactively identify systemic technical risks and resolve escalations, act as the primary technical point of contact across teams and geographies
- Drive the agile process for your team — sprint planning, estimation, and ensuring deliverables meet quality and timeline commitments
- Own projects end-to-end: from conceptualization and technical design through execution, delivery, and post-launch monitoring
- Translate ambiguous business requirements into clear technical specifications for the team to execute against
- Design, review, and optimize complex SQL queries and data-intensive operations at scale
- Architect asynchronous and distributed data workflows using message brokers (RabbitMQ, Redis)
- Define and oversee containerization (Docker) and orchestration (Kubernetes) strategy for deployment
- Guide the design of data visualization outputs and analytics deliverables for both technical and executive stakeholders
- Report project and team progress to senior leadership, with clear visibility into risks and trade-offs
- Represent the team in cross-functional collaboration with data science, product,



and infrastructure stakeholders

Skills

Primary

- Expert-level Python, with deep experience in Pandas for large-scale data manipulation (NumPy a plus)
- Practical experience with NLTK or comparable NLP libraries for text analytics
- Strong hands-on experience with Docker and Kubernetes, including production orchestration decisions
- Solid working knowledge of Redis and/or RabbitMQ for caching, queuing, and async workflows
- Strong AWS experience across compute, storage, and data services
- Django experience (good to have)

Data & Querying

- Expert-level SQL and MySQL; able to design, review, and optimize complex queries at scale
- Proven experience handling data-intensive, high-volume operations and diagnosing performance bottlenecks

Visualization & Web

- Familiarity with visualization libraries (Matplotlib, Plotly, Seaborn) or BI tooling
- Working knowledge of JavaScript, Ajax, jQuery, XML, XHTML, HTML5, CSS
- Experience integrating and architecting around third-party web services and APIs

Automation & Systems

- Strong Shell scripting for automation and operational tooling
- Excellent verbal and written communication — able to represent technical decisions to non-technical and senior stakeholders

Leadership





- Demonstrated experience leading and mentoring engineering teams
- Experience owning technical roadmaps and making architecture-level trade-off decisions
- Track record of running or significantly shaping code review culture

Desired Candidate Profile

- Master's Degree (B.Tech/MCA) or equivalent practical experience
- 5–10 years of experience, with a significant portion in a technical leadership or lead-developer capacity
- Strong programming, debugging, and systems-level problem-solving skills
- Proven ownership of code review processes and engineering standards, not just participation in them
- Solid leadership qualities — able to set direction, delegate effectively, and be accountable for team output
- Experience managing or closely coordinating with geographically distributed teams
- Comfortable operating with ambiguity and making calls when specs are incomplete

Good to Have

- Exposure to LLMs and Generative AI tools (prompt engineering, evaluation frameworks, applied use in analytics workflows)
- Experience with vector databases or embedding-based search
- Understanding of cloud-native and distributed systems design at an architectural level
- Knowledge of workflow orchestration tools (Airflow, Zookeeper, or similar)
- Familiarity with CI/CD pipelines and infrastructure-as-code
- Exposure to MLOps practices, feature stores, or data versioning
- Experience with big-data tools (Spark, Dask) for scaling beyond single-node Pandas workflows
- Prior experience as a technical lead or architect in a data/analytics-heavy organization

📌 Python Lead (Hyderabad)
🏢 Adroit India
📍 Hyderabad