Team Lead Data Engineer

We support tech companies in building engineering teams in Poland. For this role, we act as a recruitment partner handling the hiring process on behalf of our customer.

Haptiq is an AI-native enterprise solutions company with purpose-built technology for public & private companies, governments, institutions, asset managers, and family offices.

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With headquarters in New York City and four global offices, Haptiq is supported by more than 300 engineers and delivery professionals across the globe.

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By centralizing and unifying data, automating workflows, and surfacing predictive nsights, Haptiq enables organizations to scale operational excellence and generate alpha across complex enterprise environments.

We are seeking a Data Engineering Team Lead with deep expertise in cloud data architecture, data modeling, SQL, and data governance for enterprise-level systems.

• Design, develop, and maintain enterprise data architecture strategies, standards, and blueprints that support operational, analytical, and AI/ML workloads.
• Architect cloud-native data solutions across AWS (Redshift, RDS, Glue, Lake Formation) or equivalent platforms, ensuring scalability, security, and cost efficiency.
• Define and enforce data modeling standards, including dimensional modeling, denormalized schemas, OLTP/OLAP design patterns, and AI-friendly ontologies.
• Architect and oversee data transformation layers using DBT, ensuring modular, tested, and well-documented models across the analytics and reporting stack.
• Lead the design of data integration and orchestration patterns using Prefect and Airflow, including batch ETL, real-time streaming, event-driven architectures, and API-based data exchange.
• Define and implement data validation, quality control, and testing frameworks to ensure accuracy, completeness, and consistency of data across pipelines and warehouses.
• Establish data quality SLAs, monitoring, and alerting standards; design automated checks and reconciliation processes to catch issues before they impact downstream consumers.
• Establish and maintain data governance frameworks covering data quality, lineage, cataloging, classification, and access control.
• Collaborate with Data Engineers, Software Engineers, Product, and Analytics teams to translate business requirements into scalable, maintainable data designs.
• Evaluate and recommend data technologies, tools, and platforms; own the technical decision-making for data infrastructure within assigned domains.
• Design data partitioning, indexing, and optimization strategies to support high-performance queries and big data workloads.
• Define and document data contracts, schemas, and interface specifications across services and teams.
• Ensure data architectures are designed to support downstream AI/ML consumption, including feature stores, embedding pipelines, and model training datasets where applicable.
• Perform architecture reviews and code reviews to ensure adherence to data standards, optimal execution patterns, and long-term maintainability.
• Validate and cleanse data and handle error conditions gracefully.
• Mentor data engineers on best practices in data modeling, architecture patterns, and cloud data design.
• Assist with automated release management and CI/CD processes as they relate to data infrastructure and pipeline deployments.

• Experience managing and mentoring a team is required — proven experience managing, mentoring, and developing team members.
• 7+ years of experience in data architecture, data engineering, or related technical roles.
• 5+ years of experience designing and implementing cloud-based data architectures using AWS, GCP, or Azure.
• 5+ years of experience writing complex SQL queries with RDBMSes.
• 5+ years of experience developing and deploying ETL/ELT pipelines using Airflow, Prefect, or similar tools.
• Strong experience with DBT for data transformation, testing, and documentation.
• Experience with data warehouse design: OLTP, OLAP, star schemas, snowflake schemas, dimensions, and facts.
• Experience with data modeling tools and methodologies, including conceptual, logical, and physical models.
• Experience with cloud-based data warehouses such as Redshift, Snowflake, or BigQuery.
• Experience implementing data validation frameworks, quality control processes, and automated testing for data pipelines.
• Familiarity with how data architectures serve AI/ML workloads, including feature stores and vector-based retrieval patterns.
• Strong understanding of data governance, data quality frameworks, and metadata management.
• Experience with cloud-based data architectures, messaging, and analytics.
• Bachelor’s degree in Computer Science or equivalent preferred.

• Python development, including Pandas and PySpark.
• Docker.
• Kubernetes.
• CI/CD automation.
• AWS Lambda and Step Functions.
• Data partitioning.
• Databricks.
• Vector databases, such as Pinecone, Weaviate, or pgvector.
• Data mesh or data fabric architectural patterns.
• Graph databases or knowledge graph design.
• Cloud certifications.

We value creative problem solvers who learn fast, work well in an open and diverse environment, and enjoy pushing the bar for success ever higher. We do work hard, but we also choose to have fun while doing it.