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Tech Stack
Tools & technologiesAirflowBigQueryCloudPythonSparkSQL
About the role
Key responsibilities & impact- Own the end-to-end delivery of a portfolio of fixed-price client engagements
- Maintain rigorous visibility into project health across the team
- Lead scope management with discipline
- Ensure project financials are accurate and current
- Conduct structured post-engagement reviews
- Partner with sales and business development to scope new client opportunities
- Lead or oversee effort estimation for engineering deliverables
- Review and sign-off on SOW language relating to engineering deliverables
- Serve as the senior engineering point of contact for clients
- Proactively communicate project status, risks, and decisions
- Lead difficult conversations when engagements are off-track
- Lead, coach, and develop a team of solution engineers
- Assign engineers to engagements thoughtfully
- Build and continuously improve delivery playbooks, estimation templates, and reusable accelerators
Requirements
What you’ll need- 7+ years in data engineering, ML/AI engineering, or closely adjacent technical discipline
- 3+ years in an engineering leadership or delivery management role within a professional services, consulting, or systems integration firm
- Demonstrated experience owning fixed-price or outcome-based client engagements
- Track record of building and developing high-performing engineering teams in a client-delivery context
- Experience supporting pre-sales processes, including scoping, estimation, and proposal development
- Strong working knowledge of modern cloud data platforms (e.g., Databricks, Microsoft Fabric, BigQuery, etc.)
- Familiarity with AI and ML engineering — including model deployment, MLOps, and practical application of LLMs (RAG pipelines, prompt engineering, API integration)
- Proficiency in Python and SQL; hands-on familiarity with dbt, Airflow, Spark, or equivalent transformation and orchestration tooling
- Ability to read, review, and provide meaningful technical feedback on engineering work
- Understanding of DataOps principles: CI/CD for data, data quality frameworks, testing, and observability in production
Benefits
Comp & perks- Flexible work arrangements
- Professional development opportunities
ATS Keywords
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Hard Skills & Tools
data engineeringML engineeringAI engineeringPythonSQLdbtAirflowSparkMLOpsDataOps
Soft Skills
leadershipcommunicationproject managementscope managementteam developmentclient engagementrisk managementproblem-solvingcoachingcollaboration