Senior Python Data Engineer
Summary
Thoucentric, a consulting firm, seeks a Senior Python Data Engineer in Bengaluru to design, build, and optimize Python plugins for data transformation and enrichment (Pandas, NumPy), with strong unit/integration testing via pytest, CI pipeline integration, and disciplined Git workflows for client analytics projects.
Key Responsibilities
- Design,
build and maintain Python plugins that encapsulate business rules and
transformation logic such as custom aggregation/disaggregation, rule-based
data enrichment, and time-grain manipulations.
- Implement
data-cleaning, validation, mapping and merging routines using Pandas,
NumPy and other Python libraries to prepare inputs for downstream
analytics.
- Define
clear interfaces and configuration schemes for plugins (for example,
picklists for output grains and rule codes) and package them for easy
consumption by implementation teams.
- Profile
and optimize Python code paths—leveraging vectorized operations, efficient
merges and in-memory transformations—to handle large datasets with low
latency.
- Establish
and enforce coding standards, write comprehensive unit and integration
tests with pytest (or similar), and ensure high coverage for all new
components.
- Triage
and resolve issues in legacy scripts, refactor complex routines for
readability and extensibility, and manage versioning across releases.
Requirements
Must-Have Skills
- 5–7
years of hands-on experience writing well-structured Python code, with
deep familiarity in both object-oriented and functional programming
paradigms.
- Expert
mastery of Pandas and NumPy for transformations (group-by aggregations,
merges, column operations), plus strong comfort with Python’s datetime and
copy modules.
- Proven
experience designing modular Python packages, exposing configuration
through parameters or picklists, and managing versioned releases.
- Ability
to translate complex, rule-driven requirements (such as disaggregation
rules, external-table merges, and priority ranking) into clean Python
functions and classes.
- Proficiency
with pytest (or equivalent), mocking, and integrating with CI pipelines
(e.g., GitHub Actions, Jenkins) for automated testing.
- Skilled
use of Python’s logging module to instrument scripts, manage log levels,
and capture diagnostic information.
- Strong
Git workflow experience, including branching strategies, pull requests,
code reviews, and merge management.