The Role:
We're looking for a hands-on Data Engineer who hits the ground running and grows into a key technical voice on the delivery team. You'll work across diverse client datasets, genuinely ambiguous problem statements, and fast-moving build cycles, helping shape the data foundations of SynSights and our Data Accelerator offerings, while being the person junior team members actually learn from.
No defined playbook here, I'm afraid. If the idea of turning messy, real-world enterprise data into intelligent, decision-ready analytics gets you out of bed in the morning, and you can explain what you've built to a CFO just as fluently as to a fellow engineer, we'd very much like to hear from you.
What You'll Do
Data Analytics and Modelling
- Work with data from varied industries (Telecom, Retail, CPG, and others) to build analytics-ready datasets, dimensional models, and forecasting pipelines.
- Develop and refine data models; perform SQL-based analysis, understand complex data schemas, and apply external data enrichment techniques.
- Dig into structured and unstructured data to surface insights that actually mean something to the business. Not just pretty charts. Actual meaning.
- Contribute to building SynaptyX's pre-built accelerators, including the Ingestion & Standardisation Engine, Metric Engine, Business Rules Engine, and Data Model Templates.
AI Solutioning
- Translate client business requirements into functional solution blueprints for use cases such as data unification and intelligence to build analytical-ready data, conversational analytics, AI-enabled dashboards, and agentic insights delivery.
- Contribute to the build-out of SynSights, configuring data ingestion, defining semantic layers, setting metric definitions, and enabling the deterministic analytics core.
- Collaborate with AI engineers on the integration of LLM agents, NLQ interfaces, and data intelligence layers.
Data Governance and Security
- Apply data governance best practices across client engagements, including data cataloguing, lineage tracking, metadata management, access controls, and PII/data redaction.
- Support AI-readiness assessments by evaluating data quality, completeness, and governance maturity.
- Make sure analytical pipelines and data products are built with security, auditability, and enterprise compliance baked in, not bolted on as an afterthought the night before go-live.
Insight Generation and Storytelling
- Turn complex findings into clear, data-backed narratives through client presentations, research briefs, and executive summaries that land well beyond the data team.
Team Contribution
- Be the technical anchor for junior team members and raise the bar on data quality and modelling rigour
- Document reusable model templates and analytical frameworks that make the whole team sharper over time.
What You Bring
Must Have
- Bachelor's degree in Engineering, Data Science, Computer Science, or a related quantitative field (Data Science preferred).
- 2-3 years of hands-on experience in data analytics, data modelling, and/or data engineering across at least one industry. We want someone who has genuinely done the thing, not just attended the workshop.
- Strong SQL and Python/PySpark skills - you've written queries that made a senior engineer nod appreciatively
- Cloud experience on AWS (Redshift, Glue, Athena) or Azure (Synapse, Data Factory, Fabric) and/ or Database experience
- Practical knowledge of data modelling: star/snowflake schemas, dimensional design, and semantic layers.
- Experience building analytics-ready datasets, including profiling, cleansing, standardising, and documenting data for downstream AI/analytics consumption.
- Familiarity with data cataloguing, metadata management, and data governance frameworks.
- Exposure to predictive analytics or forecasting at an applied level.
- A communicator who can tell a story with data for any audience in the room — including the ones who think a data lake is somewhere in the Lake District.
Strongly Preferred
- Experience supporting AI-readiness assessments: data quality scorecards, pipeline maturity evaluations, and remediation roadmaps.
- Hands-on experience with data security practices: PII detection, data redaction, access controls, and audit trail design.
- Basic familiarity/awareness of BI tools such as Power BI, Tableau, or Qlik.
- Understanding of how the full AI/analytics stack connects: data pipelines, semantic layers, LLM interfaces, and decision outputs. All the way through, not just your bit.
Bonus points for
- Familiarity with GenAI, LLMs, or conversational analytics/NLQ interfaces.
- Exposure to data product thinking: governed, reusable datasets designed for internal or external consumption.
- Industry depth in Telecom, Retail, CPG, Airlines, or BFSI.
The Mindset We're Hiring For
A figure-it-out mindset. Someone comfortable with ambiguity, proactive in problem solving, and genuinely curious about what's next. You ask the right questions, make reasonable assumptions, ship something, and iterate. You own what you build and can defend it confidently in a client room, even when the client has opinions. Especially when the client has opinions.
Why SynaptyX
You will work on real enterprise problems, not internal projects that live and die in a PowerPoint. You will contribute to live product builds (SynSights and the Data Accelerator Stack) that are already in client hands. You will work alongside a small, senior team with Big 4 and deep industry pedigree, where your ideas are visible and your growth is your own to drive. And you will move fast. Terrifyingly fast, by most corporate standards.
✅ Do: Tell us about the messiest data problem you've solved and how you got out of it alive
❌ Don't: Send a CV with I am passionate about data and nothing else. We've read that one before.
