Team Lead-Data Engineering
Eckoh is a market leader of Customer Engagement Security Solutions, supporting an international client base from our offices in the U.S. and the U.K. Eckoh is owned by Bridgepoint one of the world's leading private asset growth investors, with over $75 billion of assets under management.
As a PCI DSS Level 1 Service Provider, our products and expanding digital and conversational AI capabilities help enterprises protect sensitive data, improve customer experience, and modernise the way they operate. Our mission is to set the standard for secure interactions between consumers and the world’s leading brands. With a strong heritage in secure payments and an expanding portfolio of cloud native, omni\-channel engagement solutions our vocation is simple: to make every customer interaction secure whether it is handled by a human agent or an AI agent, without ever compromising the customer experience.
Our large portfolio of clients, which includes many of the Fortune 250, come from a broad range of vertical markets including healthcare, retail, hospitality, financial services, and utilities.
The Role:
Eckoh is building a new Data Lakehouse on Databricks and AWS to underpin real\-time client dashboards, operational reporting, AI and data science capabilities, and compliance data management across multiple regions.
As Team Lead for Eckoh's Data Engineering function, you will own the architectural vision for this platform and lead a multidisciplinary team spanning data engineering, analytics engineering, data quality, and BI development. You will play a central part in forming that team, and will partner closely with the Head of Product, Technical Product Owner, and Head of Engineering to set delivery direction and operating rhythm.
During the initial build phase, an external delivery partner will be embedded within your team. You will direct their engineers alongside your own, ensuring shared ways of working, accelerated delivery, and continuous knowledge transfer so that platform ownership is built with Eckoh from the outset.
This is a hands\-on people management role for someone with a strong engineering background, with accountability for architecture, platform reliability, cost discipline, and an ongoing roadmap of data products.
Key Responsibilities:
People \& Team
- Line\-manage a team spanning data engineering, analytics engineering, data quality, and BI development. Own hiring, onboarding, growth, and performance across the full team lifecycle.
- Set clear expectations, run regular one\-to\-ones, and create the conditions for individuals to do their best work, develop their skills, and progress their careers.
- Build a healthy team culture across mixed seniority and skill heritage, including supporting colleagues transitioning from legacy reporting environments to modern data platform ways of working.
- Provide technical direction on platform architecture, ensuring design decisions support scalability, maintainability, and long\-term fitness for purpose.
- Define and own the end\-to\-end architecture of the Data Lakehouse, including ingestion, storage, processing, and serving layers, and the integration patterns connecting them to source systems and downstream consumers.
- Establish the data strategy and target\-state architecture from a greenfield position, making and documenting foundational decisions (batch vs streaming, data modelling approach, layering, multi\-region design) with clear rationale and trade\-offs.
- Own data modelling standards across the platform, including medallion layering and dimensional modelling for the serving layer.
- Set and govern architectural standards, patterns, and reference implementations that the team and the delivery partner build against.
- Own the team’s operating rhythm, covering planning, standups, retros, and prioritisation cadence with the Technical Product Owner.
- Track delivery against the product roadmap and strategic backlog. Manage trade\-offs, surface risk early, and protect the team from churn.
- Drive a healthy DevOps and SRE mindset across the team, covering observability, incident response, and runbook quality.
- During the initial build phase, work alongside the external delivery partner on pairing, knowledge transfer, code review participation, and runbook handover, ensuring a smooth transition to team ownership.
- Manage upward and outward stakeholder relationships across Product, Engineering, Commercial, Finance, and GRC so the team can stay focused on technical and product priorities.
- Engage directly with Databricks as the platform scales and the vendor relationship matures.
- Own the operational health of the Data Lakehouse. Define and enforce SLAs, lead incident response, and drive a culture of continuous improvement across the team.
- Partner with DevOps and CloudOps teams on deployment pipelines, platform infrastructure, and the rollout of additional data processing regions.
- Own the data governance framework for the platform, covering data classification, access control, lineage, and cataloguing across all data domains.
- Set and maintain standards for data quality, ensuring pipelines deliver accurate, complete, and timely data to downstream consumers.
- Establish and enforce data retention, archiving, and deletion policies in line with regulatory and contractual obligations.
- Ensure sensitive data, including payment and personal data, is handled appropriately throughout ingestion, processing, storage, and consumption.
- Ensure the platform meets data sovereignty, PCI DSS, SOC2, and GDPR obligations across all regions in which Eckoh operates.
- Own the cost efficiency of the platform. Monitor cloud spend, identify optimisation opportunities, and maintain a responsible operational run rate without compromising reliability or performance.
- Evaluate and introduce tooling, frameworks, and practices that improve the team’s engineering quality and delivery capability over time.
- Work closely with Product, Commercial, and client\-facing teams to ensure the platform delivers tangible value, including accurate client reporting, low\-latency dashboards, and data products that support customer outcomes.
- Identify opportunities to unlock new value from data for internal operations, revenue assurance, and client\-facing products.
- Translate business requirements into clear data platform priorities and communicate platform capabilities and constraints to non\-technical stakeholders in accessible terms.
- Demonstrable hands\-on data engineering background, with clear progression to senior engineer or tech lead before moving into people management.
- At least 3 years line\-managing engineering teams, with direct accountability for hiring, performance, and team development.
- Direct, hands\-on experience with Databricks, ideally building or operating a Databricks\-based Lakehouse including Delta Lake and Unity Catalog.
- Strong AWS data stack knowledge (e.g. S3, Glue, Redshift, Aurora, Kinesis or equivalent) and familiarity with the operational realities of running cloud platforms in production.
- Proficiency in Python and/or PySpark and SQL, sufficient to review code, guide architecture decisions, and participate credibly in technical discussions.
- Familiarity with data modelling approaches for Lakehouse architectures, including medallion architecture patterns (bronze/silver/gold) and dimensional modelling.
- Experience running an Agile delivery cadence in a product engineering environment, including prioritisation, capacity planning, and risk management.
- Experience managing an external delivery partner engagement: governance, scope management, knowledge transfer, and structured handover.
- Proven hiring experience, including defining roles, structured interviewing, and closing senior engineering candidates.
- Familiarity with data governance concepts as implemented in modern Lakehouse platforms, covering cataloguing, lineage, classification, and access control.
- Comfortable operating in a regulated environment with PCI DSS, GDPR, SOC2, or similar compliance obligations.
- Hands\-on experience with Databricks Unity Catalog, Delta Live Tables, or Databricks Workflows.
- Experience with workflow orchestration tools and CI/CD practices for data pipelines (e.g. Git, GitHub Actions, Terraform or CloudFormation).
- Experience with real\-time or streaming data pipelines (e.g. AWS Kinesis, Apache Kafka, or Databricks Structured Streaming).
- Experience managing data engineering, analytics engineering, or BI teams specifically, rather than purely software engineering teams.
- Familiarity with Tableau or similar BI and embedded analytics tools used to serve client\-facing dashboards.
- Exposure to payments, contact centre, conversational AI, or SaaS platform domains.
- Experience working alongside a systems integrator or delivery partner on a build\-and\-handover engagement.
- Coaching or mentoring qualifications, or equivalent structured approach to team development.
*Our values sit at the heart of the culture at Eckoh:*
*We encourage and support everyone to grow with Eckoh*
*We challenge, listen, and are open minded to change and suggestions from others*
*As trusted advisors, we use our knowledge to solve challenges and deliver the best for our clients*
*We take personal ownership to strive for excellence in whatever we do*
*We are welcoming, embrace diversity and respect each other in a spirit of true humanity*
*At Eckoh we value creativity, collaboration and innovation and look forward to welcoming a new team member who shares these values.*
Join Us!
So, if you are an innovator and love to find creative solutions to challenges, are passionate about helping customers pro
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