Role Description We are seeking a skilled Data Engineer with strong hands-on experience in ETL development, Databricks, PySpark, Python, cloud services, and data warehousing. The primary responsibility of this role is to design, build, and maintain scalable data pipelines and backend data services that support analytics, reporting, application integrations, and enterprise data platforms. The ideal candidate should also have backend development experience and practical exposure to modern AI-enabled data solutions, including RAG, LangChain, vector databases, embeddings, and LLM-based applications. Generative AI experience is considered an added advantage and should complement the core data engineering responsibilities. Key Responsibilities - Design, develop, and maintain scalable ETL/ELT pipelines for batch and near-real-time data processing. - Build data engineering solutions using Python, SQL, PySpark, Apache Spark, Databricks, Airflow, Matillion, DBT, and related technologies. - Develop and optimize Databricks notebooks, jobs, workflows, Spark transformations, and Delta Lake-based processing pipelines. - Ingest, transform, validate, and load data from APIs, cloud storage, databases, logs, FTP/SFTP servers, files, and enterprise applications. - Work with cloud services across AWS and/or Azure, including storage, compute, serverless processing, monitoring, logging, and managed data services. - Build and maintain backend services and APIs using Python frameworks such as Flask, FastAPI, or similar technologies to expose data, trigger pipelines, and support downstream applications. - Design and support data models, curated datasets, warehouse tables, and lakehouse layers for analytics, reporting, operational dashboards, and AI-driven use cases. - Work with modern data warehouses and databases such as Snowflake, Amazon Redshift, PostgreSQL, SQL Server, MySQL, Oracle, OpenSearch, or similar platforms. - Implement data quality checks, logging, monitoring, alerting, exception handling, and pipeline failure recovery mechanisms. - Collaborate with data analysts, data scientists, backend developers, cloud engineers, and business stakeholders to deliver reliable and production-ready data solutions. - Support GenAI-enabled data use cases where required, including RAG pipelines, document ingestion, embedding generation, vector search, and LangChain-based workflows. - Assist in integrating enterprise data with LLM applications while ensuring proper metadata filtering, access controls, tenant isolation, and grounded response generation. - Participate in CI/CD, version control, deployment, and production support activities using Git, GitHub, Jenkins, Docker, CodePipeline, ECS, or similar tools. Qualifications - 6 years of experience in data engineering, ETL development, backend data services, or cloud data platforms. - Strong hands-on experience with Python, SQL, PySpark, Apache Spark, and ETL/ELT pipeline development. - Practical experience with Databricks Data Engineering, including notebooks, jobs, workflows, Spark jobs, and scalable transformation pipelines. - Experience working with cloud platforms such as AWS or Azure. - Experience with cloud services such as AWS S3, Glue, Lambda, EMR, Redshift, RDS, ECS, EC2, SQS, Azure Blob Storage, ADLS, Cosmos DB, Azure AI Search, Key Vault, Application Insights, or Log Analytics. - Strong understanding of data warehousing concepts, dimensional modeling, data marts, curated layers, and warehouse performance optimization. - Experience working with data warehouses and databases such as Snowflake, Redshift, PostgreSQL, SQL Server, MySQL, Oracle, OpenSearch, or similar systems. - Experience building backend services or APIs using Python, Flask, FastAPI, or similar backend frameworks. - Good understanding of data ingestion patterns, file formats, data validation, schema handling, metadata management, and pipeline orchestration. - Experience with workflow orchestration and transformation tools such as Airflow, Databricks Workflows, Matillion, or DBT. - Ability to troubleshoot production data issues, optimize Spark jobs, tune SQL queries, and improve pipeline performance. - Strong documentation, communication, and cross-functional collaboration skills. GenAI / AI Add-On Skills - Working knowledge of RAG architecture, including document ingestion, chunking, embeddings, retrieval, and response generation. - Experience or exposure to LangChain, prompt orchestration, LLM integration, and AI-powered search workflows. - Familiarity with vector databases or vector search platforms such as Azure AI Search, OpenSearch, ChromaDB, FAISS, Pinecone, Weaviate, Milvus, or similar tools. - Understanding of how structured, semi-structured, and unstructured data can be prepared and indexed for GenAI applications. - Exposure to LLM platforms such as OpenAI, Azure OpenAI, Claude, AWS Bedrock, or Hugging Face. - Ability to support AI applications from a data engineering perspective by preparing, securing, indexing, and retrieving enterprise data. Preferred Qualifications - Experience building production-grade data pipelines that process large volumes of records. - Experience with Delta Lake, Medallion Architecture, data lakehouse design, and Databricks performance optimization. - Experience with backend API development for data access, pipeline triggering, metadata management, or analytics integration. - Exposure to enterprise search, document intelligence, log analytics, observability, or security data platforms. - Experience with containerized deployments using Docker, AWS ECS, Kubernetes, or similar cloud-native services. - Familiarity with CI/CD pipelines, automated deployments, release documentation, and production support practices. Technical Skills - Primary Skills: Python, SQL, ETL/ELT, PySpark, Apache Spark, Databricks, Cloud Services, Data Warehousing - Data Engineering: Airflow, Matillion, DBT, Delta Lake, Data Pipelines, Data Quality, Batch Processing, Orchestration - Cloud: AWS, Azure, Snowflake - Data Platforms: Snowflake, Redshift, PostgreSQL, SQL Server, MySQL, Oracle, OpenSearch - Backend: Flask, FastAPI, REST APIs, Python Services, API Integration - GenAI Add-On: RAG, LangChain, Vector Databases, Embeddings, LLM Integration, Prompt Engineering - DevOps: Git, GitHub, Jenkins, Docker, CodePipeline, UCD, ECS Education - Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, Artificial Intelligence, Machine Learning, or a related technical field.

Title: Senior Data Engineer Location: Toronto ON CA Job Description: About Flinks Flinks is the embedded finance platform that brings together connectivity, intelligence, and payments — giving businesses the infrastructure they need to build and deliver seamless financial experiences at scale. As a leader in Open Finance in Canada, we’ve grown since 2016 into one of North America’s most trusted platforms for financial data access, enrichment, and money movement. We work with innovators across many industries, including lending, fintech, banking, insurance, and wealth management. Today, our platform connects to 15,000+ financial institutions across North America and powers over 1M monthly connections. We also give our customers unprecedented visibility into 4,500+ real-time financial insights to support smarter decisioning. Companies rely on Flinks to streamline onboarding, verify income, assess credit risk, and power faster payment experiences. We’re on a mission to drive financial innovation and help businesses build financial experiences that feel effortless, connected, and customer-first. That’s where you come in. The Role We're hiring our Senior Data Engineer (Data / ML Platform) to stand up data engineering as a discipline at Flinks. You'll own the data and ML platform that turns models into reliable production services, harden the data models the business runs on and close the seam between our data scientists and the product teams. This is a high-ownership, greenfield-leaning role: much of this foundation is yours to build and own, not inherit. If you like being the person who makes data and ML production-grade - pipelines, serving, governance, reliability - and you want broad impact across a company's data, this is built for you. What You'll Do - Own and evolve the data platform - the BigQuery warehouse, dbt transformation layers, Airflow / Cloud Composer orchestration and Pub/Sub ingestion that feed every model and metric. - Build and operate the ML platform - training pipelines (Kubeflow on Vertex AI), model serving (FastAPI behind Vertex endpoints), CI/CD, containerization and typed contracts. Take operational ownership of model-serving infrastructure so reliability isn't carried by the data scientists alone. - Harden and standardize the data models the business depends on - improving schemas, fixing data-quality issues and establishing trustworthy source-of-truth feeds. - Establish data governance and observability - bring data that lives outside the warehouse under proper governance and build operational metrics for products that don't yet have them. - Standardize how data engineering is done across product lines - patterns, tooling and pipelines other teams can adopt. - Partner across data science, backend and product on the producer to consumer contract (models produced by data science, consumed/aggregated downstream, surfaced to clients). What You'll Work On You'll help build and evolve the data platform that powers Flinks' financial intelligence products, supporting everything from transaction enrichment and categorization to risk and payments decisioning. Key areas of focus include: - Building scalable data pipelines that process and transform large volumes of financial data. - Designing and maintaining reliable datasets, data models, and feature pipelines used by machine learning and product teams. - Improving data quality, observability, and operational metrics across our platform and customer-facing products. - Developing cost-efficient, high-performance data services and infrastructure that support real-time and batch workloads. - Partnering closely with Data Science, Product, and Engineering teams to enable new capabilities and accelerate product delivery. - Contributing to the evolution of our data platform architecture as we continue to scale our products, customers, and machine learning capabilities. Our stack - Python, SQL, Bash - Google Cloud Platform (GCP) - BigQuery and dbt - Airflow (Cloud Composer), Pub/Sub, and Cloud Functions - Kubeflow, Vertex AI, MLflow, and FastAPI - Docker, Terraform, and Protocol Buffers - Azure DevOps - Grafana and GCP Logging You don't need experience with every tool listed above - strong Data Engineering fundamentals and experience building production data platforms matter more than direct experience with our exact stack. SQL is the exception: it's a non-negotiable (see Key Requirements). Why This Role - Greenfield ownership — help build and evolve the data platform that powers Flinks' next generation of data and machine learning products. - High leverage impact — your work enables Data Science, Product, Engineering, and Risk teams to move faster with reliable, trusted data. - Real-world scale and complexity — work with large volumes of financial data powering products used by banks, fintechs, and financial institutions across North America. - Modern cloud-native environment — build on a modern GCP stack using contemporary data, platform, and machine learning tooling. Key Requirements - Experience: 5+ years of hands-on Data Engineering experience designing, building, and operating production data platforms, pipelines, and warehouse solutions in a cloud environment. - Data Engineering Expertise: Strong experience with ETL/ELT development, data modeling, schema design, orchestration, data quality, lineage, and warehouse optimization. Experience with BigQuery, dbt, Airflow, or equivalent modern data tooling is highly desirable. - Technical Foundation: Expert SQL and strong Python skills, with the ability to build scalable, maintainable, and well-tested data solutions that support both operational and analytical workloads. - Cloud Data Platforms: Experience working with modern cloud-native data ecosystems, including data warehouses, event-driven architectures, distributed processing, and platform observability. - Operational Excellence: Demonstrated ownership of production systems, including monitoring, reliability, performance tuning, cost optimization, incident response, and ongoing platform improvements. - Machine Learning Platform Exposure: Experience supporting machine learning workflows, feature pipelines, model-serving infrastructure, or MLOps environments is an asset, but a strong Data Engineering foundation is the primary requirement. - Collaboration: Ability to partner effectively with Data Science, Product, Engineering, and QA teams to deliver trusted, scalable, and well-governed data solutions. - Education: Bachelor's degree in Computer Science, Data Engineering, Software Engineering, or a related technical field, or equivalent practical experience. - Work Authorization: Must be legally authorized to work in Canada. Compensation Range For experienced and qualified hires located in Canada, of senior (IC4) level, the compensation range is between $120,000 to $160,000 CAD annually. As part of the total rewards package, Flinks offers: - Health & Dental coverage as of Day 1 - Flexible Paid Time Off (FTO) - Remote work environment with frequent in-person gatherings and activities. - Career development, learning opportunities and growth - And more We are committed to providing accommodations for persons with disabilities. If you require accommodation, we will work with you to meet your needs. Flinks uses artificial intelligence (AI) during the recruitment process to assist in screening, assessing, or selecting applicants.

• Design and evolve the platform’s canonical data model • Architect and implement scalable data ingestion and synchronization frameworks • Build reliable, replayable, and highly scalable data processing pipelines • Design entity resolution and identity management capabilities • Define and implement data governance, lineage, and provenance standards • Drive architectural decisions related to scalability, resiliency, security, and operability • Collaborate with product and engineering teams to define platform capabilities and roadmap • Design APIs and delivery mechanisms for downstream consumers • Support customer migrations from legacy systems into the new platform • Mentor engineers and contribute to technical leadership across the team • Ensure platform reliability, performance, and operational excellence

At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. In data engineering at PwC, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for developing and implementing data pipelines, data integration, and data transformation solutions. Growing as a strategic advisor, you leverage your influence, expertise, and network to deliver quality results. You motivate and coach others, coming together to solve complex problems. As you increase in autonomy, you apply sound judgment, recognising when to take action and when to escalate. You are expected to solve through complexity, ask thoughtful questions, and clearly communicate how things fit together. Your ability to develop and sustain high performing, diverse, and inclusive teams, and your commitment to excellence, contributes to the success of our Firm. Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to: Craft and convey clear, impactful and engaging messages that tell a holistic story. Apply systems thinking to identify underlying problems and/or opportunities. Validate outcomes with clients, share alternative perspectives, and act on client feedback. Direct the team through complexity, demonstrating composure through ambiguous, challenging and uncertain situations. Deepen and evolve your expertise with a focus on staying relevant. Initiate open and honest coaching conversations at all levels. Make difficult decisions and take action to resolve issues hindering team effectiveness. Model and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance), the Firm's code of conduct, and independence requirements. The Opportunity As part of the Data and Analytics Engineering team, you will design and implement thorough data architecture strategies that enhance healthcare operations. As a Senior Manager, you will influence key stakeholders, making sure that innovative data solutions meet the evolving needs of health systems while developing productive teams committed to excellence. Responsibilities - Design and execute strategic data architecture strategies for healthcare - Influence and engage key stakeholders to align on innovative data solutions - Develop and mentor dynamic teams focused on operational excellence - Analyze and address evolving needs within health systems through data insights - Lead initiatives that enhance healthcare operations through impactful data management - Foster an environment of collaboration and open communication among teams - Uphold compliance with industry regulations and standards in data practices - Drive strategic improvements in data architecture to support organizational goals What You Must Have - Bachelor's Degree - At least 8 years of experience What Sets You Apart - Hands-on experience with EMR back-end databases preferred - Familiarity with HL7 v2, FHIR R4, and interoperability standards - Proven track record architecting data solutions for healthcare programs - Navigating HIPAA compliance and PHI data handling requirements - Bridging clinical, operational, and technical conversations - Experience with healthcare-specific integration engines and middleware - Applying Gen AI techniques to healthcare use cases - Proficiency in Python or SQL for data transformation - Understanding of how data architecture supports quality reporting The salary range for this position is: $124,000 - $280,000. Actual compensation within the range will be dependent upon the individual's skills, experience, qualifications and location, and applicable employment laws. All hired individuals are eligible for an annual discretionary bonus. PwC offers a wide range of benefits, including medical, dental, vision, 401k, holiday pay, vacation, personal and family sick leave, and more. To view our benefits at a glance, please visit the following link: https://pwc.to/benefits-at-a-glance As PwC is an equal opportunity employer, all qualified applicants will receive consideration for employment at PwC without regard to race; color; religion; national origin; sex (including pregnancy, sexual orientation, and gender identity); age; disability; genetic information (including family medical history); veteran, marital, or citizenship status; or, any other status protected by law. PwC does not intend to hire experienced or entry level job seekers who will need, now or in the future, PwC sponsorship through the H-1B lottery, except as set forth within the following policy: https://pwc.to/H-1B-Lottery-Policy. Learn more about how we work: https://pwc.to/how-we-work For only those qualified applicants that are impacted by the Los Angeles County Fair Chance Ordinance for Employers, the Los Angeles' Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, San Diego County Fair Chance Ordinance, and the California Fair Chance Act, where applicable, arrest or conviction records will be considered for Employment in accordance with these laws. At PwC, we recognize that conviction records may have a direct, adverse, and negative relationship to responsibilities such as accessing sensitive company or customer information, handling proprietary assets, or collaborating closely with team members. We evaluate these factors thoughtfully to establish a secure and trusted workplace for all. Applications will be accepted until the position is filled or the posting is removed, unless otherwise set forth on the following webpage. Please visit this link for information about anticipated application deadlines: https://pwc.to/us-application-deadlines #LI-Hybrid #BI-Hybrid