Data Platform Engineer – Python / Azure / ETL

Location: Bengaluru, In-office

Type: Full-time

Experience: 1–4 years

About Kaitongo

Kaitongo is a Canadian GenAI-powered market intelligence startup that helps client-facing professionals cultivate and nurture client relationships through relevant, timely market insights.

Our platform helps professionals prepare for client engagements, track market and client developments, and proactively reach out to clients and prospects with credible, personalized insights.

We are now building out our external and internal data platform to support richer client intelligence, signal generation, relationship mapping, and AI-enabled workflows.

About the Role

We are hiring a Data Platform Engineer – Python / Azure / ETL to support the data engineering layer behind Kaitongo’s market intelligence platform.

This is not a pure AI developer role. The role is focused on data pipelines, ETL workflows, Azure-based deployment, Python-based business logic, data quality, and platform reliability.

You will work closely with our backend, AI, and product teams to help connect external and internal data sources, operate ETL workflows, manage data movement into Kaitongo’s systems, and help build internal data-processing capability using Azure, Databricks, Python, and related data engineering tools.

A key part of the role will involve working with third-party ETL/data platforms, while also helping Kaitongo in building internal data pipelines, validation logic, and reusable transformation modules.

What You Will Work On

1. ETL and Data Pipeline Support

• Support ingestion of data from external and internal sources, including APIs, files, databases, CRM exports, and structured/unstructured datasets.
• Help clean, normalize, deduplicate, validate, and enrich incoming data.
• Build Python scripts and pipeline components for data transformation and quality checks.
• Map raw source data into Kaitongo’s internal schemas and downstream systems.
• Track pipeline failures, missing fields, malformed data, duplicate records, and low-confidence matches.
• Maintain documentation and runbooks for data flows, dependencies, and operational processes.

2. ETL Workflow Platform Operations

• Support the deployment and operation of ETL workflow platforms in Kaitongo’s Azure environment.
• Understand platform inputs, transformations, outputs, and integration points.
• Monitor ETL jobs and troubleshoot issues with data ingestion, transformation, and delivery.
• Work with the team to validate the quality and completeness of the platform outputs.
• Help document platform workflows so Kaitongo has clear visibility and operational control.

3. Python Logic Layer

• Build Python-based business logic modules that sit between ETL outputs and Kaitongo’s product/backend systems.
• Support entity matching, company-name normalization, client/account mapping, and data enrichment.
• Help implement rules for matching news, companies, sectors, contacts, lawyers, firms, and client accounts.
• Build validation logic to check data freshness, source reliability, confidence scores, and duplicate records.
• Create reusable utilities for parsing, transformation, matching, and structured output generation.
• Work with backend and AI engineers to make processed data usable for search, RAG, signals, and product workflows.

4. Azure Data Platform Work

• Work with Azure services used for data storage, processing, and deployment.
• Use Azure Blob Storage / Azure Data Lake Storage for storing raw, processed, and enriched datasets.
• Help build scheduled and event-driven jobs using Azure Functions or similar services.
• Support experiments with Azure Databricks for ETL, transformation, and large-scale data processing.
• Work with Azure Key Vault, service principals, managed identities, and environment configuration under guidance.
• Help monitor jobs and services using Azure logs and observability tools.
• Support cost-conscious and secure use of Azure resources.

Required Skills

• Strong Python programming skills.
• Good working knowledge of SQL.
• Understanding of data cleaning, transformation, validation, and ETL concepts.
• Experience working with APIs, JSON, CSV, databases, and structured datasets.
• Ability to write clean, reusable, and well-documented code.
• Basic understanding of cloud platforms; Azure experience is preferred.
• Familiarity with Git and collaborative development workflows.
• Strong debugging and problem-solving ability.
• Willingness to work with messy real-world data and improve it systematically.
• Ability to learn quickly and work closely with senior engineers, product teams, and leadership.

Preferred Skills

• Experience with Azure Blob Storage, Azure Functions, Azure Data Factory, or Azure Databricks.
• Experience with pandas, PySpark, or similar data-processing tools.
• Experience with PostgreSQL, OpenSearch, Elasticsearch, Weaviate, or other data/search stores.
• Familiarity with data lakes, Delta tables, or lakehouse concepts.
• Experience with CI/CD using GitHub Actions or Azure DevOps.
• Basic understanding of authentication, secrets management, service principals, and managed identities.
• Exposure to LLMs, embeddings, RAG, or vector search is useful but not mandatory.
• Experience working with CRM, market intelligence, news, legal, financial, or professional-services data is a plus.

The core of this role is data engineering, ETL support, Python-based logic, Azure data services, and operational reliability.

Education

Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field. Equivalent practical experience will also be considered.

Ideal Candidate

The ideal candidate is a hands-on engineer who enjoys working with data pipelines, Python scripts, cloud services, and real-world business logic.

You do not need to be an AI expert, but you should be curious about how clean, structured, and reliable data powers AI products.

You should be comfortable learning fast, debugging issues patiently, documenting your work, and building reliable data foundations for a growing product.