👋 About Reelo :
Reelo is an all-in-one customer data, loyalty, and marketing automation platform trusted by 5,000+ brands big and small across 9 countries and growing. Brands like Punjab Grill, Jumboking, Pop Tates, The Beer Cafe, Hakkasan, Roastery Coffee House, and many more use Reelo to grow faster.
Founded in 2021 by brother duo Parin & Prit Sanghvi, Reelo helps restaurants and retail businesses maximize their revenue and growth through a unified platform for loyalty, marketing, and data analytics. With Reelo, businesses can market like Starbucks, without the effort or expertise, driving more repeat customers, increasing sales, and building a stronger brand.
As competition for customer attention intensifies, it has never been more important for restaurants and retail businesses to have a 360° view of their customers, the very people who keep them in business, and to engage with them using data to drive loyalty and revenue.
Coming from a family of retail entrepreneurs, we understand this deeply: while it’s easier than ever to start a business, it’s harder than ever to grow one.
Competition is fierce. Standing out is increasingly difficult, and businesses feel this pressure every day. Customer acquisition costs are rising, retention is challenging, and there’s too much to juggle, too many tools, too many disconnected solutions. Most existing platforms are complex, expensive, and built for sophisticated marketers at large enterprise brands.
We identified two key challenges:
- Small and mid-sized businesses don’t have the time to do data-driven marketing.
- Those that do marketing often see poor results.
Reelo is on a mission to change this, permanently !!!
Our SaaS platform makes it fast and easy for businesses to understand their customers and continuously engage with them in a smart, automated way, bringing them back again and again, and building lasting customer relationships.
There is a massive opportunity in front of us. We’re building a business that is big, meaningful, and lasting. For that, we’re building a strong, diverse team of curious, creative people who want to find purpose in their work and support each other in the process. If that sounds like you, we’d love to hear from you.
You can learn more about us here:
https://reelo.io/
https://reelo.io/blog/reelo-raises-1-million-round-led-by-veteran-silicon-valley-investor-gokul-rajaram/
About the role
We’re hiring a Data Engineer to own our data platform end to end: the pipelines that move data from MongoDB into ClickHouse, the accuracy of every number our customers see, and the performance and cost of ClickHouse itself.
You’ll work on our live production stack and use AI-assisted development (Claude Code) as part of your everyday workflow. You’ll be the first responder when something in your area breaks, so you’ll care about root causes, not restarts.
What you’ll own
1. Data pipelines
- Run and improve the event flow from MongoDB through Kinesis, SQS and Lambda consumers, plus ClickPipes, into ClickHouse.
- Keep consumers healthy: retries, batch item failures, dedup, dead-letter queues, and Mongo and Redis connection limits.
- Fix failures quickly and find the root cause. A restart doesn’t count as a fix.
- Keep dev, staging and prod schemas in sync, and handle DDL and schema initialisation safely.
2. Data accuracy and reconciliation
- Own reconciliation between MongoDB (source) and ClickHouse (reports) for orders, feedbacks, loyalty points, credits and comm reports.
- Investigate customer-reported mismatches (points balances, sales totals, undelivered counts, segment counts) and close each one with a root cause.
- Build checks that catch drift before customers do: daily row counts, sums and key metrics compared per brand.
- Get timezone, order_type and status handling right everywhere a metric is computed.
3. Moving to incremental processing
- Replace full recompute on every event with incremental updates, using ClickHouse materialized views and AggregatingMergeTree instead of repeated MongoDB aggregations.
- Ship migrations in small, switchable steps, each verified against the old path before cut-over.
- Retire legacy pipeline paths (Kinesis → Lambda → ClickHouse) once the replacements are proven correct.
4. ClickHouse optimisation
- Own schema design: ORDER BY, partition keys, granularity and engine choice, following ClickHouse best practices.
- Tune the slowest and most expensive queries weekly using system.query_log, and keep the ClickHouse Cloud bill within target.
- Prevent merge pressure: no forced whole-partition merges, sensible insert batching, healthy part counts.
- Make sure dashboard and export endpoints meet their latency targets.
On-call: Each of these four areas carries its own on-call responsibility. When it breaks, you’re the first responder.
Must have
- Strong ClickHouse and analytical SQL (CTEs, window functions, argMax, joins)
- Production experience running SQS or Kinesis pipelines in Node.js
- Solid MongoDB experience (aggregations, indexes, change data)
- Hands-on use of Claude Code or a similar AI coding tool, with the discipline to verify AI output through tests and real data before shipping
Nice to have
- CDC tools such as PeerDB or Debezium
- EMR Serverless or Spark
- Python for data checks
- Experience moving batch recompute to incremental processing
Our stack
- AI-assisted development: Claude Code
- Pipelines: AWS SQS, Kinesis Data Streams, Lambda, Step Functions
- Analytics database: ClickHouse Cloud, ClickPipes (CDC)
- Source database: MongoDB (Mongoose)
- Compute and deploy: AWS ECS Fargate, Serverless Framework, EventBridge Scheduler, S3, SSM
- Caching and queues: Redis, Bull
- Language: Node.js (JavaScript)
- Observability: Datadog, AWS CloudWatch
What success looks like
- Customer-reported data mismatches are rare, and when they happen they’re closed with a root cause.
- Drift is caught by your checks before customers notice.
- Metrics run on incremental paths, and the old pipelines are retired.
- Dashboards and exports hit their latency targets, and the ClickHouse bill stays on target.
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