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analytics

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Agency job
via Bean HR Consulting by snigdha Khurana

Gurugram, Pune, Bengaluru (Bangalore) · 6 - 10 years · Posted 13 Oct 2026

Data modeling
data model
Slowly changing dimensions
Dimensional modeling
ERwin
+1 more

Hiring: Data Modeler

Experience: 6+ years

Location: Bangalore/Gurugram/Pune


Required Skills

  • Hands-on experience in conceptual, logical, and physical data modeling.
  • Strong knowledge of ER modeling, dimensional modeling, star/snowflake schemas, fact/dimension tables, and SCDs.
  • Strong SQL skills for data profiling, validation, and quality checks.
  • Experience with source-to-target mapping, data dictionaries, metadata, and data lineage.
  • Proficiency in Erwin, ER/Studio, or similar modeling tools.
  • Exposure to Snowflake, Databricks, Redshift, SQL Server, or other data platforms.
  • Understanding of data governance, UAT, reconciliation, and Agile methodologies.
  • Strong analytical, documentation, and stakeholder management skills.


Preferred Qualifications

  • Experience in enterprise data warehousing or lakehouse modeling.
  • Knowledge of data products, data contracts, quality SLAs, and reusable datasets.
  • Exposure to dbt, data cataloging, metadata management, and governance tools.
  • Ability to work independently in a fast-paced environment.


If interested please share your cv at : snigdhaattheratebeanhr.com

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Noida · 6 - 10 years · ₹25L - ₹35L / yr · Profitable · Posted 19 Sep 2025

skill iconPostgreSQL
Apache Kafka
CI/CD
Apache Airflow
Slowly changing dimensions
+2 more

Title: Data Platform / Database Architect (Postgres + Kafka) — AI‑Ready Data Infrastructure

Location: Noida (Hybrid). Remote within IST±3 considered for exceptional candidates.

Employment: Full‑time


About Us

We are building a high‑throughput, audit‑friendly data platform that powers a SaaS for financial data automation and reconciliation. The stack blends OLTP (Postgres), streaming (Kafka/Debezium), and OLAP (ClickHouse/Snowflake/BigQuery), with hooks for AI use‑cases (vector search, feature store, RAG).


Role Summary

Own the end‑to‑end design and performance of our data platform—from multi‑tenant Postgres schemas to CDC pipelines and analytics stores—while laying the groundwork for AI‑powered product features.


What You’ll Do

• Design multi‑tenant Postgres schemas (partitioning, indexing, normalization, RLS), and define retention/archival strategies.  

• Make Postgres fast and reliable: EXPLAIN/ANALYZE, connection pooling, vacuum/bloat control, query/index tuning, replication.  

• Build event‑streaming/CDC with Kafka/Debezium (topics, partitions, schema registry), and deliver data to ClickHouse/Snowflake/BigQuery.  

• Model analytics layers (star/snowflake), orchestrate jobs (Airflow/Dagster), and implement dbt‑based transformations.  

• Establish observability and SLOs for data: query/queue metrics, tracing, alerting, capacity planning.  

• Implement data security: encryption, masking, tokenization of PII, IAM boundaries; contribute to PCI‑like audit posture.  

• Integrate AI plumbing: vector embeddings (pgvector/Milvus), basic feature‑store patterns (Feast), retrieval pipelines and metadata lineage.  

• Collaborate with backend/ML/product to review designs, coach engineers, write docs/runbooks, and lead migrations.


Must‑Have Qualifications

• 6+ years building high‑scale data platforms with deep PostgreSQL experience (partitioning, advanced indexing, query planning, replication/HA).  

• Hands‑on with Kafka (or equivalent) and Debezium/CDC patterns; schema registry (Avro/Protobuf) and exactly‑once/at‑least‑once tradeoffs.  

• One or more analytics engines at scale: ClickHouse, Snowflake, or BigQuery, plus strong SQL.  

• Python for data tooling (pydantic, SQLAlchemy, or similar); orchestration with Airflow or Dagster; transformations with dbt.  

• Solid cloud experience (AWS/GCP/Azure)—networking, security groups/IAM, secrets management, cost controls.  

• Pragmatic performance engineering mindset; excellent communication and documentation.


Nice‑to‑Have

• Vector/semantic search (pgvector/Milvus/Pinecone), feature store (Feast), or RAG data pipelines.  

• Experience in fintech‑style domains (reconciliation, ledgers, payments) and SOX/PCI‑like controls.  

• Infra‑as‑Code (Terraform), containerized services (Docker/K8s), and observability stacks (Prometheus/Grafana/OpenTelemetry).  

• Exposure to Go/Java for stream processors/consumers.  

• Lakehouse formats (Delta/Iceberg/Hudi).

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