About Incode

Incode is the leading provider of world-class identity solutions that is reinventing the way humans authenticate and verify their identities online to power a world of digital trust.

Through our revolutionary identity solutions, we are unleashing the business potential of universal industries including finance, government, retail, hospitality, gaming, and more, by reducing fraud and transforming human interactions with data, products, and services.

We're in the process of rapidly scaling our diverse global team and we're looking for entrepreneurial individuals and leaders who are curious, driven, and excited by ownership to join a Unicorn-status scale-up!

Incode is a Series B unicorn rewriting how the world proves identity. Our AI-powered platform lets leading banks, fintechs, marketplaces, and governments deliver friction-free experiences while defeating fraud and safeguarding privacy. Customers such as Citi, AirBnB, Block, Chime, Sixt, and TikTok rely on Incode to power their identity verification and security.

Recently named a Leader in the Gartner® Magic Quadrant™ for Identity Verification, we're scaling fast — and we're looking for data professionals passionate about investigating data quality problems and building the systems that keep world-class ML models reliable.

The Impact You'll Make

As a Data Analyst on the ID Document Intelligence team, you'll investigate data quality issues, perform root cause analysis, and build the pipelines and automated checks that keep Incode's machine learning ecosystem reliable. You'll ensure that data flows efficiently and accurately through every stage of model training, labeling, and performance tracking.

Your work will be essential to maintaining the scalability, quality, and precision of Incode's document intelligence systems used by millions worldwide.

What You'll Own & Drive

• Root Cause Investigation — Independently investigate data quality issues end-to-end. When a model metric drops or data looks wrong, you own the investigation: forming hypotheses, querying across data sources, and delivering a clear, evidence-backed answer.
• Automated Data Pipelines — Design, build, and maintain pipelines for collection, labeling, validation, and metric computation that support ML training and evaluation.
• Data & Labeling Quality Standards — Establish and monitor consistency checks, accuracy audits, and root-cause analysis when issues impact model outcomes.
• Model Evaluation Metrics — Define, implement, and automate evaluation metrics and reporting that reflect real-world product use cases and business goals.
• Performance Tracking Systems — Build scalable dashboards and monitoring to enable fast, data-driven decisions across teams.
• Workflow Orchestration — Develop and operate reliable orchestration (Airflow, Prefect, or similar) to schedule, observe, and troubleshoot end-to-end pipelines.
• Clean, Maintainable Code — Write SQL and Python to efficiently investigate problems — querying databases, calling internal APIs, and processing data across multiple sources.
• Cross-Functional Partnership — Partner closely with ML engineers, analysts, and product stakeholders to prioritize work by impact, unblock execution, and continuously improve internal tooling for analysis and evaluation.

The Qualities That Set You Apart

• Investigative mindset — You don't stop at the symptom. You chase data anomalies until you find the root cause, and you bring receipts.
• Statistical intuition — You can tell the difference between a meaningful metric shift and noise, and you know how to prove it.
• Builder's bias — You'd rather automate a check than run it manually for the third time.
• Proactive ownership — You spot problems before anyone flags them, and you drive them to resolution without waiting for permission.
• Clear communicator — You translate messy data into crisp answers that ML engineers, PMs, and leadership can act on.

Your Background

• 3+ years of experience as a Data Analyst or in a similar data infrastructure role.
• Strong SQL and Python skills for data investigation and root cause analysis.
• Hands-on experience with AWS Redshift or a similar columnar/cloud database (BigQuery, Snowflake, etc.).
• Solid statistical foundation — you can reason about rates, distributions, significance, and sampling bias.
• Hands-on experience with workflow orchestration tools (Airflow, Prefect, Dagster, etc.).
• Proven experience in data quality management, data preparation, or ML data pipelines.
• A proactive mindset toward identifying problems.
• Strong collaboration and problem-solving skills.
• Background in mathematics, physics, or engineering.

Preferred Experience

• Familiarity with big data technologies or data infrastructure optimization.
• Experience with labeling workflows or ML data preparation pipelines.
• Exposure to AWS or other cloud-based data solutions.
• Interest in machine learning operations (MLOps) and scalable ML systems.

Why Incode?

• Mission with Meaning — Build systems that enable ethical, seamless identity verification for millions.
• Rocket-Ship Growth — Join a company scaling globally with AI at its core.
• Elite Team & Technology — Collaborate with top engineers and data scientists redefining document intelligence.
• Ownership & Autonomy — Operate with end-to-end responsibility for impactful data pipelines.
• Global Impact — Your work will power real-world AI experiences trusted by major enterprises.

Power a world of trust.

Main Purpose of Job

This is a senior hands-on data engineering role supporting the delivery, maintenance, and improvement of our client's data platform and data warehouse services.
The role combines hands on development with technical oversight of distributed delivery teams. It is responsible for designing and maintaining reliable data pipelines, ETL processes, data models, APIs, and supporting AWS based services, while ensuring delivery aligns with development standards, architecture principles, and operational expectations.
The role will work across technologies including AWS S3, AWS Lambda, AWS Fargate, AWS Glue, Amazon Redshift, SQL, and Python. It will also provide day to day technical guidance and review for offshore development teams, helping ensure that work is correctly understood, appropriately designed, well implemented, tested, and suitable for long term support.

Knowledge, Skills and Experience

Essential Knowledge

• Proven experience as a Senior Data Engineer, Data Warehouse Developer, Data Engineer, or similar technical data role.
• Experience developing and maintaining data warehouse solutions and ETL processes.
• Strong understanding of data modelling, data transformation, and data quality principles.
• Experience working with cloud based data engineering services.
• Strong understanding of software engineering good practice, including maintainability, version control, testing, review, and documentation.
• Experience working in Agile delivery environments.
• Experience working with distributed, offshore, near shore, or third party development teams.
• Ability to provide technical guidance and challenge implementation decisions constructively.
• Ability to challenge proposed designs and implementations where they create risk around maintainability, performance, data quality, security, or supportability.
• Effective communication skills, with the ability to work with both technical and non technical stakeholders.
• Familiarity with Jira and Confluence for project and delivery management.

Essential Functional / Technical Skills

• Strong SQL skills, including query optimization, data transformation, modelling, reconciliation, and validation.
• Strong Python skills for data engineering, automation, ETL, or API development.
• Experience with AWS S3 for data storage and data exchange.
• Experience with AWS Glue or similar ETL tooling.
• Experience with Amazon Redshift or similar cloud data warehouse technology.
• Experience with AWS Lambda, AWS Fargate, or similar serverless/container-based services.
• Experience developing, supporting, or integrating APIs used by data platforms or downstream systems.
• Experience with data quality checks, validation, reconciliation, and production support of data pipelines.
• Familiarity with GitHub or similar version control tools.
• Understanding of CI/CD, environment management, deployment safety, and operational support.

Desirable

• Experience providing technical oversight to offshore or near shore delivery teams.
• Experience with infrastructure as code tools such as Terraform.
• Experience with Java or other backend development languages.
• Familiarity with AWS IAM, secrets management, access control, and cloud security fundamentals.
• Experience with data governance, data lineage, and data catalogue tooling.
• Experience with reporting, BI, or analytics platforms.
• Experience working in high traffic, consumer, ecommerce, media, sport, or membership-based organizations.

What we offer:

•     Dynamic and supporting international teams.
•     Regular assessments and performance reviews. You will have the opportunity for promotion, bonuses and a raise in accordance with the pace at which you develop and your performances.
•     Hybrid or office work.
•     20-25 vacation days per year.
•     4 days of sick leave (100% paid)
•     Equipment for work, laptop and all necessary additions.
•     Access to trainings and courses.
•     Private health insurance.
•     FIT Pass card for many sports’ facilities

The Analytics Engineering Technical Lead will define and own the modeling architecture, standards, and technical direction within RSI’s cloud-native Data Platform. This role is accountable for ensuring that analytics-ready data is reliable, scalable, and aligned to long-term business needs across growth, operations, and risk.

You will lead the design and governance of core data models in Snowflake and dbt, establishing best practices that enable high-quality, self-service analytics without sacrificing trust or performance. As a hands-on technical leader, you will set standards for modeling, testing, documentation, and deployment while mentoring other Analytics Engineers and raising the overall technical bar of the team.

This role partners closely with Data Engineering, Analytics, and business stakeholders to translate evolving requirements into durable data products. You will help balance speed and governance, prioritize platform investments, and ensure our data foundation scales with the business.

Our modern data stack includes Snowflake, AWS, Python, Airflow, and dbt.

What You'll Do:

• Lead the design, development, and lifecycle management of curated datasets, dbt models, and analytics data marts aligned to business domains and metrics.
• Establish and evolve standards and best practices for dbt modeling, testing, documentation, and code organization to support long-term scalability.
• Ensure analytical datasets are accurate, reliable, and trusted, serving as a single source of truth for business, product, and stakeholders.
• Embed data quality checks, documentation, and governance directly into analytics pipelines.
• Partner closely with Product, Data Engineering and Analytics teams to translate business requirements into well-modeled, performant data products.
• Mentor and support Analytics Engineers through technical guidance, design reviews, and knowledge sharing.
• Continuously improve and modernize analytics workflows by introducing automation, tooling enhancements, and AI-assisted development where appropriate.
• This role includes technical leadership and mentoring responsibilities for the team, including other Analytics Engineers.

What You'll Bring:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related quantitative discipline.
• Technical Leadership: Proven experience defining technical direction, standards, and architectural decisions within analytics engineering or curation teams.
• Data Modeling Expertise: Deep understanding of dbt, data marts, dimensional modeling, and self-service BI enablement.
• Data Engineering Background: 7+ years of experience in data warehousing or development; 5+ years of experience in hands-on data engineering using SQL and Python.
• Cloud Infrastructure: Proficiency with cloud platforms (AWS, GCP, or Azure), including containerization (Docker/Kubernetes).
• Tooling & Orchestration: Experience with tools like dbt, Airflow, S3, REST APIs, and large-scale DWHs (Snowflake, Redshift, BigQuery, etc.).
• Change Management: Skilled at leading cross-functional data initiatives and driving adoption of new tools and standards.
• People Leadership: Experience mentoring and guiding distributed teams across time zones.
• Communication: Strong written and verbal English skills with the ability to collaborate across technical and business functions.
• Able to travel occasionally both domestically and internationally #LI-HYBRID