Lead Python Developer Data Analytics Visualization

Udyora Careers; Hyderabad; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Udyora Careers is seeking a senior data analytics engineer to own the technical architecture for analytics apps. You will lead a team of engineers, drive feature development, and set coding standards while delivering scalable data pipelines.

You will design data workflows, optimize SQL queries, and guide deployment with Docker/Kubernetes. Strong Python, NLP, and AWS experience are essential for success.

Qualifications

• Master's degree or equivalent practical experience.
• 8–12 years of experience in technical leadership or lead developer roles.
• Proven ownership of code reviews and engineering standards.
• Experience with distributed data systems and analytics pipelines.

Responsibilities

• Own the technical architecture for data analytics and visualization apps using Python.
• Mentor and grow a team of engineers across maintenance, new features, and analytics.
• Make key architecture and tooling decisions for data pipelines and storage.

Skills

Python

Pandas

NLP

SQL

MySQL

Visualization (Matplotlib/Plotly/Seab4

Education

Master's Degree (B.Tech/MCA)

Tools

Docker

Kubernetes

Redis

RabbitMQ

AWS

Django

Airflow

Zookeeper

Spark

Job description

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
• 8–12 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
• Strong 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

Values We're Looking For

• Ownership at scale — accountable not just for their own output, but for the team's collective delivery and quality
• Judgment under ambiguity — makes sound architectural and prioritization calls without waiting for perfect information
• Mentorship as a core function — actively grows junior and mid-level engineers, not just reviews their work
• Pragmatism over perfectionism — chooses the simplest solution that meets the bar, and can defend when added complexity is actually justified
• Transparent communication — surfaces risks and trade-offs early, to the team and to leadership, rather than after the fact
• Curiosity and currency — stays ahead of tooling and practice