Data Engineering Tech Lead

As Data Engineer Lead, you'll own the design, reliability, and evolution of Bisviews data platform.

Some data infrastructure is already in place, so we're looking for someone who can confidently assess what exists, fix what's broken, and build a scalable foundation for analytics, trust and safety, and product features.

You'll set technical direction for data engineering, guide other engineers, and collaborate closely with backend engineers, product, design, QA, and leadership to turn raw platform data into trustworthy, usable information.

Key Responsibilities

1. Data Platform Audit & Strategy

Review and assess the existing data sources, pipelines, storage, and reporting setup.

Identify gaps, data quality issues, technical debt, and performance or cost bottlenecks.

Define and document the target data architecture and a phased roadmap to reach it.

Establish data engineering standards, conventions, and documentation.

2. Data Pipelines & Infrastructure

Design, build, and maintain reliable batch and streaming pipelines (ETL/ELT).

Ingest data from the core application database, third-party services, and event tracking.

Build and manage the BigQuery data warehouse that powers analytics and product features.

Orchestrate, schedule, and monitor workflows with clear alerting and recovery.

Optimise pipelines for performance, scalability, and cloud cost.

3. Data Modelling & Analytics Enablement

Design clean, well-documented data models for reviews, businesses, users, reports, and trust signals.

Build curated datasets and metrics layers for product, growth, and operations dashboards.

Partner with product and leadership to define core KPIs and ensure one source of truth.

Support self-serve analytics through BI tooling.

4. Trust, Safety & Data Products

Provide the data foundations for fake review detection, fraud signals, and business trust scoring.

Work with backend engineers to expose data products to the platform through APIs or services.

Support ML or rules-based models with feature pipelines and well-labelled training data.

5. Data Quality, Governance & Security

Implement data quality checks, testing, lineage, and observability.

Enforce access controls, PII handling, retention, and secure data practices.

Ensure compliance with data protection regulations across the regions we operate in (e.g. GDPR, NDPR).

6. Leadership & Delivery

Lead and mentor a team of data engineering interns: assign and plan their work, review their code, and support their growth.

Translate business requirements into technical plans, estimates, and priorities.

Participate in sprint planning, engineering discussions, and team meetings.

Provide regular progress updates and communicate risks and blockers early.

Support deployments and troubleshoot production data issues.

Our Tech Stack

Google Cloud Platform (GCP)

BigQuery (data warehouse)

Cloud Storage

Pub/Sub and Dataflow (streaming and processing)

Cloud Composer (Airflow) for orchestration

dbt (transformation)

Python and SQL

PostgreSQL / Cloud SQL

Looker Studio (BI)

Docker and Git-based CI/CD

Requirements

6+ years of professional data engineering experience, with at least 2 years in a lead or senior ownership role.

Strong proficiency in Python and advanced SQL.

Proven experience designing and running production data pipelines and warehouses.

Hands-on experience with Airflow (ideally Cloud Composer) or similar orchestration tools.

Experience with dbt or similar transformation and modelling frameworks.

Strong understanding of data modelling (dimensional, event-based) and warehouse design.

Hands-on experience with GCP data services, especially BigQuery, Cloud Storage, Pub/Sub, and Cloud Composer.

Experience with data quality testing, monitoring, and observability.

Solid understanding of data security, PII handling, and data privacy regulations.

Proficiency with Git and collaborative development workflows.

Experience auditing and improving existing data systems, not only building from scratch.

Experience mentoring junior engineers or interns.

Excellent problem-solving, communication, and stakeholder management skills.

Ability to work independently and lead effectively in a remote environment.

Nice to Have

Experience with streaming pipelines using Pub/Sub, Dataflow (Apache Beam), or Kafka.

Experience supporting machine learning, fraud detection, or trust and safety systems.

Familiarity with BI tools such as Looker Studio, Looker, or Metabase.

Experience with infrastructure as code (Terraform) and containerisation.

Familiarity with search and indexing tools such as Elasticsearch or OpenSearch.

Experience with multi-country or multi-region data and compliance.

Previous experience on marketplaces, review platforms, or other consumer-facing products.

Initial Expectations

During the initial engagement, you'll be expected to:

Audit the existing data sources, pipelines, and reporting setup, and document key findings.

Identify and prioritise data quality issues, technical debt, and architectural improvements.

Propose a target data architecture and a phased roadmap.

Stabilise critical pipelines and establish monitoring and alerting.

Deliver a reliable core dataset and the first set of agreed business metrics.

Take ownership of ongoing data engineering work and set team standards.

Who We're Looking For

We're looking for a data engineer who takes ownership, thinks critically, and knows how to improve existing systems without unnecessarily rebuilding what already works.

You should be comfortable navigating unfamiliar data and codebases, making sound architectural decisions, leading by example, growing junior talent, and explaining technical trade-offs to non-technical stakeholders.

If you're passionate about building trustworthy data systems and enjoy solving complex data challenges, we'd love to hear from you.