Technical Lead Data

About the role

We are building EIQ, a platform product that enterprises use, among other things, to make their data governed, secured, controlled, discoverable, curated, processed, and delivered across every stage of the data lifecycle. We don't just move data, we operationalize the full Variety Velocity Volume Value chain while keeping Visibility and Veracity intact and Vulnerability minimized.

This is a product engineering role, not a solutions role. You will not be building pipelines for a single business. You will be building the platform on which thousands of pipelines, models, and data products are designed, deployed, monitored, and trusted. The distinction matters: every component you build is a capability shipped to customers, not a one-off integration. Reusability, configurability, multitenancy, observability, and extensibility are first-class concerns in everything you write.

What you'll do at EvoluteIQ:

You will l lead engineering on one or more areas of the EIQ data processing stack:

• Data Producer and Ingestion layer: Connectors and ingestion engines spanning batch, mini batch, real-time streaming, CDC, pub-sub, queues, and ESB integrations across SQL/NoSQL sources, SaaS apps, IoT/edge, clickstreams, and event/webhook streams. Structured, semistructured, and unstructured. Cloud and on-premise.
• Data Processing engine: Wrangling, transformation, ETL/ELT, stream processing, microbatching, enrichment, compaction, and workflow orchestration as configurable platform capabilities.
• Data Storage abstraction: A unified interface across data lakes, warehouses, lakehouses, object stores, graph DBs, and row/columnar stores with schema management, compression, and caching built in.
• ML enablement: The substrate on which feature engineering, model training, evaluation, tuning, refinement, and MLOps run as productized workflows.
• Cross-cutting platform capabilities: Metadata management (registry, cataloguing, lineage, tagging, version control), data quality and integrity (accuracy, freshness, deduplication, lifecycle, MDM, testing), and data security (authN/authZ, encryption at rest and in transit, policies, compliance, observability, auditing, alerting).

Key Responsibilities:

• Lead technical execution for one or more pillars of the EIQ platform: Translating the platform's architectural direction into concrete designs for storage abstractions, processing engines, connector frameworks, and metadata models that scale to enterprise customers across industries.
• Build for multi-tenancy, extensibility, and configurability: Every feature should generalize across customer workloads, not solve one use case.
• Lead a team of data and platform engineers as their technical anchor. Set the technical bar, run design reviews, unblock the team, and mentor on tradeoffs between performance, cost, reliability, and developer experience.
• Drive component-level build-vs-buy-vs-integrate decisions within the platform's architectural direction. Know when to leverage Spark/Kafka/Airflow/dbt/Iceberg/Delta and when to build proprietary capability that differentiates the product.
• Productize governance, security, and observability as platform-native features not bolt ons. The components your team ships must be auditable, lineage-aware, and policy-enforcing by default.
• Partner closely with the architect, Product Management, Data Science, and Design to translate customer-facing capabilities into engineering plans your team can execute against. Bring engineering reality into architectural discussions; bring architectural intent into your team's day-to-day.
• Understand the persona using each feature data engineer, analyst, scientist, steward, security officer and make sure that's reflected in how the team designs APIs, SDKs, and UX.
• Establish engineering excellence CI/CD, automated testing across unit/integration/contract layers, release packaging, performance benchmarking, SLO-driven observability, and incident response.
• Be a technical voice in competitive positioning. Understand what competitors do well, where they fall short, and where EIQ wins.

What will you bring to the team?

• 10+ years in data engineering, with at least 23 years as a tech lead or senior engineer guiding a team. Bonus if some of that time was building a product shipped to external customers rather than internal pipelines.
• Deep expertise in SQL, Python (Scala or Java a plus), and the modern big data ecosystem Spark, Kafka, Airflow, Flink, dbt, Iceberg/Delta/Hudi.
• Hands-on with at least one major cloud (AWS, Azure, or GCP) and ideally experience designing for cloud-agnostic or hybrid deployment.
• Strong grasp of data modeling, warehousing, lakehouse architecture, and realtime/streaming processing and the tradeoffs between them.
• Platform thinking. You've designed systems with APIs, SDKs, plugin architectures, multitenancy, or self-service workflows not just DAGs that move data from A to B.
• Governance and security as instinct. You understand lineage, cataloguing, access controls, encryption, and compliance frameworks (GDPR, HIPAA, SOC 2) as engineering requirements, not afterthoughts.
• Proven technical leadership running design reviews, growing engineers, driving delivery across complex, ambiguous projects, and earning trust as the senior engineer in the room.
• Excellent communication. You can explain a partitioning strategy to a junior engineer and a roadmap tradeoff to an architect or PM in the same afternoon.

Good To Have:

• Experience building or contributing to a data platform product (Fivetran, Hevo, Airbyte, Databricks, Snowflake, Confluent, Estuary, Matillion, or similar).
• Familiarity with metadata and catalog tools (DataHub, Amundsen, OpenMetadata, Collibra, Alation).
• Background in MLOps platforms or feature stores.
• Open-source contributions to the modern data stack.

What Sets This Role Apart

Most "lead data engineer" roles are about wiring data flows inside one company. This one is about leading engineering on the product that other companies' data engineers will use to wire their flows at scale, under governance, with security and lineage baked in. You won't own the platform architecture, but you will own how a meaningful part of that platform actually gets built, shipped, and made trustworthy in production. If that distinction excites you, we should talk.

What We Offer

• Opportunity to shape the strategy of a next-gen agentic automation platform.
• Work with a cross-disciplinary team in a fast-growing, innovation-driven environment.
• Competitive compensation and growth opportunities.
• A culture of innovation, ownership, and continuous learning.

We value a range of diverse backgrounds, experiences, and ideas. We pride ourselves on our diversity and inclusive workplace that provides equal opportunities to all persons regardless of age, race, color, religion, sex, sexual orientation, gender identity and expression, national origin, disability, neurodiversity, military and/or veteran status, or any other protected classes.