Data Ops Engineer

Overview

: 4.00 + years

Salary: Confidential (based on experience)

Expected Notice Period: 7 Days

Shift: (GMT+05:30) Asia/Kolkata (IST)

Opportunity Type: Remote

Placement Type: Full Time Contract for 6 Months(40 hrs a week/160 hrs a month)

(*Note: This is a requirement for one of Uplers' client - LL)

What do you need for this opportunity?

Must have skills required:

Backend, insight workflows, Staging, Airbyte, Data Mapping, Data Ops, ETL, PostgreSQL, Python

LL is Looking for:

Overview

We are supporting an innovative early-stage SaaS company building a first-of-its-kind AI insights platform for commercial teams. The platform ingests and analyses customer conversations along with sales and marketing data to generate actionable insights—automating strategic workflows that are traditionally manual. With the MVP built and about to be trialled by customers, the team is looking for an experienced Data Ops Engineer to scale ingestion pipelines and ensure high data reliability. This role offers the opportunity to shape the data foundations that power downstream analytics and AI workflows, ensuring ingestion, validation, and traceability at scale.

About The Project

The Data Ops Engineer will design, implement, and manage ingestion pipelines across structured and unstructured client data sources. This includes extending robust pipelines using Airbyte, Cloud Composer, and custom Python logic to ensure reliable, validated ingestion into the core data model.

You will work closely with Backend, ML, and Product teams to ensure ingestion aligns with downstream AI insight use cases. The role also involves defining tagging logic, metadata strategies, and data quality checks within a modern, GCP-native infrastructure.

This role is well suited to someone who enjoys building practical, resilient data systems in lean, fast-moving environments.

Current Phase: Post-MVP Growth & Scaling

The MVP is complete and going live with initial users. The focus has shifted from “building the first version” to scalability, reliability, and automation. The Data Ops Engineer will expand ingestion coverage to new data sources, improve observability, and lay the technical foundation for rapid multi-client onboarding.

The Role

You will own the ingestion foundations of the platform, evolving early MVP scripts into robust, scalable pipelines. Your goal is to ensure ingestion is reliable, traceable, and well-structured—enabling high-quality downstream analytics and AI-driven insights.

Must-Have Experience & Skills

Technical / Product

• 4+ years of relevant Data Engineering / Data Ops experience
• Strong experience with ETL / ELT tooling (e.g. Dataflow, BigQuery, Cloud Composer, Cloud Functions)
• Proficiency in Python for data manipulation and ingestion logic
• Experience with PostgreSQL for relational data models and metadata handling
• Experience working with APIs (file systems, messaging tools, enterprise platforms)
• Strong hands-on experience with orchestration tools such as Airbyte or Cloud Composer
• Ability to design and document data mapping and tagging logic
• Understanding of observability best practices (logs, alerts, health metrics)
• Familiarity with staging, operational, and analytical data environments
  
  
  

• Experience working in early-stage SaaS environments, particularly analytics or AI-driven platforms
• Experience supporting ML teams or insight workflows through clean data architecture
• Ability to reason about business-relevant metadata, validation, and traceability
• Comfortable influencing tooling and infrastructure choices in an evolving product
  
  
  

• Pragmatic, delivery-oriented mindset with the ability to iterate quickly
• Strong problem-solving and debugging skills
• Clear, proactive communicator who flags risks early
• Collaborative, low-ego team player across backend, ML, and product teams
• Comfortable operating in ambiguous, high-ownership environments
  
  
  

• Pipeline Scaling: Extend and automate ingestion connectors for diverse sources (email, transcripts, files, conversational tools) using Airbyte or Cloud Composer
• Data Quality & Governance: Own data validation, schema alignment, and error monitoring; ensure data models support RBAC, analytics, and AI retrieval
• Metadata Strategy: Maintain standardised metadata and traceability from source → insight to support analytics and AI use cases
• Observability: Partner with DevOps to implement logging, alerting, and ingestion health diagnostics
• Collaboration: Work closely with domain experts to align taxonomy and business logic
  
  
  

• Ingestion pipelines are robust, validated, and scalable across multiple clients
• Metadata and tagging logic are consistently applied and traceable
• Errors and schema mismatches are proactively identified and handled
• Stakeholders can clearly track what data was ingested, when, and from where
• Ingestion outputs support downstream AI workflows and insight tagging
  
  
  

• Equipment: BYOD
• Onboarding: Intro sessions with engineering, product, and delivery leads
• Eligibility: Candidates must not be based in regions subject to UK financial sanctions
  
  
  

• Step 1: Click On Apply! And Register or Login on our portal.
• Step 2: Complete the Screening Form & Upload updated Resume
• Step 3: Increase your chances to get shortlisted & meet the client for the Interview!
  
  
  

Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement.

(Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well).

So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!