About InSync Analytics

InSync Analytics is a leading fintech company specializing in proprietary company models, custom consensus estimates, and outsourced analyst services. We provide high-quality, bespoke financial modelling solutions, ranging from standard financial analysis to cutting-edge, customized solutions tailored to our clients' needs.

Position Overview

You will own the data pipelines that bring transactional and third-party data into our warehouse and serving layers, build the APIs and services that expose that data to internal teams and clients, and keep the whole chain observable and trustworthy end to end, from the moment a record changes in a source system to the moment it lands in front of an analyst.

- The interesting problems here are about scale, speed, and correctness under change. We work with large volumes of data flowing in continuously from multiple, disparate source systems, and latency is not a nice-to-have, because stale figures during market hours are a real problem. On top of that, we increasingly need this data structured and curated so it is AI-ready: clean, well-documented, and directly consumable by models and agents, not just dashboards. You will design pipelines and validation layers that hold up under that volume and speed, and catch problems before they reach a client.

Key Responsibilities

Data Pipelines and CDC

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Design, build, and operate batch and streaming data pipelines that move data from operational systems into the data warehouse and downstream services.

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Build change-data-capture (CDC) pipelines using AWS Glue, Kafka (on-premise or Amazon MSK), and Kinesis to stream inserts, updates, and deletes from source databases with low latency.

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Use Amazon S3 as the data lake and landing layer for raw and processed data, with a sensible partitioning and lifecycle strategy as volumes grow.

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Build event-driven processing with AWS Lambda, and orchestrate multi-step pipeline workflows with AWS Step Functions, managing dependencies, retries, and failure handling without manual intervention.

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Design ingestion and transformation jobs that are idempotent, replayable, and resilient to out-of-order or late-arriving events, and that hold up under high-volume, multi-source data flowing in continuously.

- Handle backfills and reprocessing without downtime to consumers.

- • Structure and curate pipeline outputs so they are AI-ready: clean, well-documented, and directly consumable by downstream models and agents, not just dashboards and reports.

Databases and Data Warehousing •
- Design and manage OLTP schemas on PostgreSQL that support high-throughput application workloads with

clean, well-normalised data models. •

Design and optimise OLAP schemas and queries on ClickHouse and Snowflake for large-scale analytical workloads and time-series financial data.

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Model data for both transactional and analytical use cases, and make deliberate trade-offs between the two rather than forcing one schema to serve both.

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Tune query performance, partitioning,



and clustering strategies as data volumes and query patterns grow.

API and Service Development

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Design and build RESTful APIs and internal services in Python that expose pipeline outputs to other teams, the web application, and external clients.

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Use Celery and Redis for asynchronous and scheduled work such as bulk exports, data refreshes, and long-running transformation jobs.

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- Write clean, modular, well-tested, production-grade Python code, and contribute to shared libraries used across the data platformb

REQUIREMENTS [2 to 4 years] of ex perience as a Data Engineer, Backend Engineer with a data focus, or in a similar role.

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Hands-on experience building data pipelines with AWS Glue, and streaming/event data with Kafka (on-premise or MSK) or Kinesis, including CDC patterns.

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Experience with core AWS data infrastructure (S3, Lambda, and Step Functions) for storage, event-driven processing, and workflow orchestration.

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Solid experience with PostgreSQL for transactional (OLTP) workloads, and with an OLAP/analytical warehouse such as ClickHouse or Snowflake.

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Experience working with large volumes of data from multiple, disparate sources, in environments where latency is a first-class concern, not an afterthought.

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Strong Python skills, with experience building RESTful APIs and using Celery and Redis for asynchronous processing.

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Practical experience with data validation, reconciliation, or data quality frameworks in a production pipeline.

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- Experience with observability and monitoring for data systems, such as logging, metrics, alerting, or tracing tools.

📌 Data Engineer (Mumbai)
🏢 Insync Analytics
📍 Mumbai