Why this Role Exists

At Blip, we believe data is at the heart of our business success, powering decisions and creating value across the organization. We are looking for a dynamic and experienced Sr Data & Analytics Engineering Manager to lead our Data Platform, Governance, and Architecture teams. This senior leadership role is crucial in driving the development and evolution of our cutting-edge data systems while ensuring compliance, security, and scalability.

We're looking for a Senior Data & Analytics Engineering Manager to lead the teams responsible for Data Platform, Governance, and Architecture, translating data strategy into organizational execution and ensuring our data foundations are robust, scalable, and deeply integrated into the company's business and product fabric.

This role sits at the intersection of people leadership, technical strategy, and cross-functional partnership, owning the outcomes of a high-performing data engineering organization and driving the evolution of Blip's data capabilities from the ground up.

Your Mission

You'll build and lead the teams that power Blip's data ecosystem, from platform infrastructure and data modeling to governance and AI enablement.

You'll translate strategic intent into organizational execution: hiring the right people, setting the right direction, and creating the conditions for engineering excellence at scale.

Key Responsibilities

Platform Evolution & Systems Architecture

• Own the vision and roadmap for the Data Platform, Governance, and Architecture teams, aligning priorities with business and product goals.
• Drive the design, implementation, and continuous improvement of a scalable, secure, and efficient data platform supporting both transactional and analytical workloads.
• Partner with Platform, AI, and Product Engineering leaders to ensure data architecture supports current and future business needs.
• Translate complex organizational and technical trade-offs into clear decisions with measurable business impact.
  
  

Team Development & Engineering Culture

• Build, mentor, and manage a high-performing team of data engineers, analytics engineers, and architects.
• Foster a culture of engineering excellence, continuous improvement, and accountability, anchored in an Engineering First Mindset.
• Establish clear career development paths, performance expectations, and coaching practices across the team.
• Drive talent acquisition and onboarding to scale team capabilities in line with platform ambitions.

Governance, Security & Compliance

• Define and enforce data governance policies that ensure accuracy, integrity, and security across the organization.
• Partner with legal, compliance, and domain teams to maintain adherence to GDPR, LGPD, and CCPA requirements.
• Ensure governance frameworks are operationalized, not just documented, through tooling, automation, and team practices.

Architecture & Technical Direction

• Guide the design of a flexible, future-proof data architecture that supports complex business models and analytical workloads.
• Provide technical oversight across ingestion, processing, curation, and serving layers without being the individual implementing them.
• Act as the organizational bridge between strategic architectural decisions and their execution by staff and senior engineers.

Cross-functional Collaboration & Strategic Impact

• Operate as a key leader across Data, Product, Engineering, and Infrastructure domains, influencing roadmaps, priorities, and organizational decisions.
• Represent the Data organization in company-wide initiatives around modernization, AI enablement, and platform evolution.
• Drive a culture of experimentation and learning, staying current on industry trends to keep Blip's data capabilities at the forefront.

What We're Looking For

Experience

• 8+ years in Data Engineering, Analytics Engineering, or related disciplines, with at least 3 years in a people management or team leadership role.
• Proven experience leading and scaling data teams across platform, governance, or architecture domains.
• Track record of delivering measurable outcomes in reliability, data quality, cost optimization, or engineering productivity through organizational execution.
• Experience driving or participating in large-scale data platform modernization initiatives.
• Demonstrated ability to influence cross-functional stakeholders and translate business requirements into technical direction.

Technical Foundations

• Deep familiarity with modern data platform technologies and cloud environments, particularly GCP; working knowledge of Azure and/or AWS.
• Solid understanding of distributed data systems, data pipelines, and ELT/ETL patterns.
• Proficiency in SQL and data modeling, with a focus on scalable analytical solutions.
• Working knowledge of data governance frameworks, data quality and observability practices, and compliance requirements.
• Familiarity with infrastructure-as-code, CI/CD practices, and developer experience patterns in data environments.

Bonus Points

• Experience operating in or transitioning toward a data mesh or federated data architecture model.
• Familiarity with agentic or AI-assisted data workflows and their implications for platform design.
• Background in FinOps applied to data infrastructure at scale.

Why Join Us

You’ll join a company with global ambitions and a highly motivated Data team, working to reshape how businesses interact with customers through conversational AI and intelligent platforms. Here, you won’t just build systems, you’ll help shape the foundation of what’s next.

Informações adicionais

Why this Role Exists

At Blip, data doesn’t just inform decisions, it powers experiences. Every message, transaction, and insight flows through our data ecosystem, and we’re scaling it to handle billions of daily events with sub-second intelligence.

We’re looking for a Staff Data Engineer to shape and evolve the backbone of our real-time data processing layer, someone capable of designing and building data-intensive systems that serve as the foundation for analytics, AI, and intelligent automation at global scale.

Your Mission

You’ll design and implement streaming and batch data systems that move and transform information reliably, efficiently, and transparently across Blip’s platform.

You’ll champion data modeling excellence, ensuring the data we produce is high-quality, discoverable, and ready for real-time consumption — by both humans and machines.

Key Responsibilities

Data Engineering & Pipeline Architecture

• Design, implement, and optimize high-throughput, low-latency data pipelines capable of processing billions of events per day.
• Build scalable data ingestion and transformation frameworks leveraging Kafka, Flink, Spark, Airflow, Beam, and/or GCP related data engineering technologies.
• Ensure fault-tolerance, exactly-once semantics, and replayability in all event processing flows.
• Establish reusable patterns and accelerators for pipeline development, deployment, and monitoring.

Modeling & Storage Design

• Design data models across streaming, analytical, and serving layers (Bronze/Silver/Gold, event sourcing, CDC, and dimensional schemas).
• Partner with Data Architects to evolve domain-driven data models ensuring semantic alignment and traceability from source to consumption.
• Implement optimal partitioning, clustering, and indexing strategies for performance and cost efficiency across Databricks, BigQuery and other storage & processing engines.
  
  

Reliability, Quality & Observability

• Embed observability, lineage, and data quality validations into every pipeline.
• Define and enforce SLAs and SLOs for data delivery, freshness, and accuracy.
• Collaborate with Platform Engineers to integrate metrics, alerts, and self-healing mechanisms for critical data flows.

Collaboration & Leadership

• Act as a technical leader within the Data Engineering team: leading design reviews, conducting architecture spikes, and mentoring engineers.
• Work closely with AI, Platform, and Product teams to deliver event-driven capabilities that power intelligent products in real time.
• Promote craftsmanship, performance, and simplicity as core engineering values.

What We’re Looking For

Experience

• 6+ years in Data Engineering, Software Engineering, or Distributed Systems roles.
• Proven experience building and operating data-intensive and streaming systems in production.
• Hands-on experience with Apache Kafka, Flink, Spark Streaming, Databricks & GCP related data engineering technologies.
• Strong understanding of event-driven architectures, change data capture (CDC), and real-time data modeling patterns.
  
  

Technical Foundations

• Proficiency in Python or PySpark, and solid command of SQL.
• Experience with cloud-native data services (GCP, Azure, or AWS) and workflow orchestration (Airflow/Composer).
• Deep understanding of data modeling and storage formats (Delta, Parquet, Avro).
• Experience with CI/CD for data pipelines, testing frameworks, and infrastructure automation (Terraform).
  
  

Bonus Points

• Experience with event sourcing or CQRS-style systems for transactional and analytical data flows.
• Knowledge of feature store architectures, real-time machine learning serving, or agentic data pipelines.
• Contributions to open-source streaming frameworks or performance optimization initiatives.

Informações adicionais

Why this Role Exists

At Blip, data doesn't just inform decisions—it powers experiences. But data at scale demands discipline: governance that enables (not blocks), costs that are predictable, and standards that accelerate (not slow down) teams.

We're migrating from Azure/Databricks to Google Cloud Platform with a clear mandate: build it right from day one. No legacy debt. No ungoverned pipelines. No cost surprises.

We're looking for a Staff Data Engineer with a rare blend: deep hands-on technical skills in data platforms plus strategic thinking in governance, cost optimization, and enablement. Someone who can turn policies into code, transform Google PSO artifacts into reusable templates, and scale best practices across domains—without becoming the "policy police."

Your Mission

You'll be the technical backbone of our Data & Analytics Center of Excellence (CoE), translating governance strategy into automation, patterns, and tooling that make "doing the right thing" the fastest and easiest path for data teams.

You'll work hand-in-hand with our Data Architect and Google Cloud Professional Services to:

• Implement governance-as-code
• Embed FinOps discipline into every pipeline, dataset, and model from day one.
• Build reusable templates, accelerators, and frameworks that scale across domains 
• Enable federated governance: empowering  teams with self-service tooling, guardrails, and observability.

Key Responsibilities

Governance Engineering & Automation

• Design and implement governance-as-code frameworks using GCP-native tools
• Build automated workflows for data classification, lineage tracking, quality validation, and compliance reporting (LGPD, GDPR, ISO 27001).
• Create reusable Infrastructure-as-Code (IaC) modules (Terraform) for governance foundations: project structure, IAM roles, security policies, audit logging, retention policies.
• Implement the Governance Seal framework: automated validation that datasets/pipelines meet minimum standards (quality, security, documentation, cost efficiency) before entering production.

FinOps for Data & Cost Optimization

• Embed FinOps best practices into CoE templates: partitioning strategies, clustering, storage classes (Standard/Nearline/Coldline/Archive), lifecycle policies, query optimization, slot reservations, autoscaling.
• Build cost monitoring and alerting for GCP data services.
• Identify, remediate and direct cost waste: unused tables, redundant pipelines, inefficient queries, over-provisioned resources.

CoE Platforms, Templates & Accelerators

• Transform deliverables (TDDs, IaC modules, pipeline patterns, KT sessions) into production-ready CoE standards: documented, versioned, tested, and reusable.
• Build a catalog of pipeline patterns and templates for common use cases: batch ingestion (JDBC, APIs, files), streaming (Pub/Sub, Kafka), CDC, data contracts, gold layer serving (fast + analytical).
• Develop self-service toolkits for Data Stewards and domain engineers: project setup scripts, data contract generators, quality rule templates, lineage visualization, metadata enrichment.
• Establish CI/CD patterns for data pipelines: automated testing (schema validation, data quality, security checks), deployment gates (Governance Seal validation), rollback strategies.

Data Modeling & Storage Design (Governance Lens)

• Design reference data models that embed governance from the start: clear ownership, semantic definitions, access policies, retention rules, lineage traceability.
• Optimize storage and compute for both performance and cost: partitioning, clustering, indexing, compression (Parquet, Avro), Delta/Iceberg table formats.
• Define standards for data contracts (schemas, SLAs, ownership, change management) and implement them as code (Avro schemas, Protobuf, dbt contracts, data validation frameworks).

Observability, Quality & Reliability

• Build data quality checks 
• Implement end-to-end data lineage (Dataplex, OpenLineage, custom metadata extraction) from source systems to analytics/AI consumption.
• Build monitoring and alerting for governance KPIs: policy compliance, data freshness, quality SLA breaches, access anomalies, cost overruns.
• Define and enforce SLAs/SLOs for data delivery, accuracy, and availability—across raw, trusted, and gold layers.

Enablement & Leadership

• Act as technical mentor and sparring partner for Data Stewards, domain engineers, and analytics teams adopting CoE standards.
• Lead design reviews and architecture spikes for governance, FinOps, and platform patterns.
• Produce playbooks, runbooks, and hands-on labs: "How to set up a governed project in GCP," "FinOps checklist for BigQuery," "Implementing dynamic data masking," etc.
• Facilitate Community of Practice sessions: share patterns, troubleshoot blockers, celebrate wins, iterate on feedback.

What We’re Looking For

Experience

• 6+ years in Data Engineering, Platform Engineering, or Data Infrastructure roles, with at least 2 years focused on governance, data quality, or platform enablement.
• Proven experience implementing data governance frameworks in production: metadata management, access control, data quality, lineage, compliance.
• Hands-on experience with FinOps for data platforms: cost monitoring, optimization, chargeback/showback models, budgeting.
• Experience building reusable frameworks, templates, or internal platforms that other engineers consume (not just one-off pipelines).

Technical Foundations

• GCP expertise (required): BigQuery (IAM, policies, partitioning, clustering, masking), Dataplex (lakes, zones, catalog, lineage, quality), Cloud Storage, Pub/Sub, Dataproc, Composer/Airflow, Cloud DLP, IAM.
• Infrastructure-as-Code: Terraform (or equivalent) for provisioning and managing GCP data infrastructure at scale.
• Programming: Strong Python and SQL; ability to write clean, testable, maintainable code for data pipelines and automation.
• Data modeling: Deep understanding of dimensional modeling, event sourcing, CDC, slowly changing dimensions, data contracts, schema evolution.
• CI/CD for data: Experience with automated testing, deployment pipelines, version control (Git), and release management for data products.
• Data quality & observability tools: Great Expectations, dbt, Dataplex Data Quality, OpenLineage, or similar.

Governance & Compliance Knowledge

• • Understanding of data privacy regulations (LGPD, GDPR) and how to implement technical controls: data classification, masking, anonymization, retention, audit trails.
  • Experience with Data Loss Prevention (DLP) tools and techniques: scanning, tokenization, redaction, de-identification.
  • Familiarity with Data Mesh or federated governance models: domain ownership, data as a product, self-serve platforms, governance as guardrails (not gates).

Bonus Points

• Experience with Azure/Databricks → GCP migrations, especially governance and cost remediation during migration.
• Knowledge of ML governance: feature stores, model lineage, experiment tracking, model monitoring, responsible AI practices.
• Contributions to open-source data governance or quality frameworks (Great Expectations, dbt packages, OpenLineage, Amundsen, Datahub, etc.).
• Experience with FinOps certification or formal FinOps programs (cloud cost management, budget forecasting, optimization strategies).
• Background in compliance audits or enterprise data governance programs (ISO 27001, SOC 2, PCI-DSS).

Why Join Us

You'll join a company with global ambitions and a highly motivated Data team at a critical inflection point: migrating to a modern cloud stack and building the governance, cost discipline, and enablement culture that will scale for years to come.

Here, you won't just implement tools—you'll architect the foundations of how Blip governs, optimizes, and democratizes data. You'll work directly with Google Cloud Professional Services, shape corporate standards, and enable dozens of engineers and analysts to build faster, safer, and smarter.

This is a rare opportunity to be the "governance engineer" who makes governance loved, not feared.

Informações adicionais