Lead Data Warehouse Engineer

FairMoney

. Own the architecture of the cloud data warehouse, from ingestion to published datasets used by analysts, BI tools and downstream systems .

Posted 10/27/2026full-timeBengaluru • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in data engineering and cloud data warehousing, with a strong focus on data quality, governance, and automation. Proven ability to lead and grow engineering teams while ensuring compliance with financial regulatory standards.

Highest-signal resume keywords

Data Engineering ManagementExpert SQLCloud Data Warehouse ExperienceData GovernanceStakeholder Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonData ModellingDimensional ModellingIncremental TransformationsIdempotent TransformationsCDCStreaming IngestionData QualityObservabilityGenAI / LLM Tooling

Soft Skills

Team LeadershipCoachingStakeholder CommunicationDecision MakingProblem Solving

Tools & Technologies

BigQuerySnowflakeRedshiftDbtAirflowGoogle Cloud Platform

Industry Keywords

FintechBankingFinancial ServicesData ContractsData Lineage

Tech Stack

Tools & technologies

AirflowAmazon RedshiftBigQueryCloudGoogle Cloud PlatformKafkaPythonSnowflakeSQL

About the role

Key responsibilities & impact

Own the architecture of the cloud data warehouse, from ingestion to published datasets used by analysts, BI tools and downstream systems
Lead modernisation of the data platform, including the move to CDC and streaming ingestion
Decide where business logic belongs and keep published datasets stable
Manage compute and storage cost and deliver measurable efficiency gains
Set and enforce standards for data modelling, transformation patterns, testing, documentation and code review
Build data quality and observability practices
Run design review, QC evidence and peer review processes before production changes
Drive automation and CI/CD across the data stack
Own freshness, accuracy and availability SLAs, on-call and incident response for business-critical datasets
Guide the team through complex distributed data pipeline issues
Lead, hire, coach and grow a team of data engineers
Set intake, triage, sprint planning, design reviews and stakeholder reporting rhythms
Partner with business, product, risk and finance leaders
Translate business requirements into reliable data products and explain trade-offs
Ensure data handling meets financial regulatory, governance and security requirements
Prepare the warehouse for AI use cases with documented models, consistent metrics and a semantic layer
Bring AI-assisted engineering and analytics into daily team work

Requirements

What you’ll need

10+ years in data engineering, data warehousing or analytics engineering, including 3+ years directly managing engineers
Expert SQL and strong Python
Deep data modelling experience: dimensional modelling, slowly changing dimensions, and incremental and idempotent transformations
Hands-on experience with a modern cloud data warehouse such as BigQuery, Snowflake or Redshift
Production experience with dbt and a workflow orchestrator such as Airflow
End-to-end ownership of business-critical datasets, with accountability for freshness, correctness and SLAs
Experience in fintech, banking or financial services
A record of hiring, retaining and growing strong engineers
Strong stakeholder management, including the ability to say no and explain why
Experience working with Google Cloud Platform
Experience working with CDC or streaming ingestion (Kafka, Debezium or similar)
Experience with data governance, data contracts and lineage
Indepth knowledge on data quality and observability tooling
Semantic layers and GenAI / LLM tooling on structured data
Leading teams across time zones

Benefits

Comp & perks

Training & Development
Performance Bonus