8 - 10 years of experience

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 : 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.

- 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 AWS experience across compute, storage, and data services.

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 - 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.

- 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 :

- Understanding of cloud-native and distributed systems design at an architectural level.

- Knowledge of workflow orchestration tools (Airflow, Zookeeper, or similar).

- 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 shifts (e.g., LLM-assisted workflows) and brings relevant ones into the team.