TE-1521- Data Engineer

Softobiz; Hyderabad; On-site; INR 4,500,000 - 7,000,000; Full time

Job summary

Softobiz is hiring a Staff Data Engineer in Hyderabad to own an end-to-end data platform—from raw ingestion to analytics marts used by business teams. You will ensure accuracy, freshness, and trust in numbers across systems, partnering with engineering, analytics, product, and external data providers.

This hands-on IC role emphasizes a canonical customer key, a well-modelled warehouse, and reverse‑ETL for activation. Strong SQL, Python, dbt, and BigQuery expertise are essential.

Qualifications

• 8–12 years in data engineering or analytics engineering at consumer-facing digital businesses.
• Deep hands-on experience designing and maintaining a data warehouse on BigQuery, Snowflake or Redshift.
• Production experience with dbt — Core or Cloud — including model layering, incremental strategies, tests, documentation, exposures, and multi-contributor repos.
• Fluent SQL — advanced window functions, CTEs, pivoting, SCD patterns.
• Experience designing identity resolution in the warehouse.
• Hands-on experience building marts for revenue, orders, subscriptions, cohorts, LTV and funnel analysis.
• Python for data work — transformations, orchestration DAGs, small internal tools.
• Knowledge of at least one workflow orchestrator — Airflow, Prefect, Dagster, or equivalent.
• Comfort with reverse-ETL tooling (Polytomic, Hightouch or Census).
• A bias for data contracts and tests over heroics.

Responsibilities

• Own end-to-end data platform—from ingestion to analytics marts.
• Partner daily with engineering, analytics, product, growth and external data partners.
• Ensure a single canonical key across systems and a well-modeled warehouse.
• Maintain data quality, freshness, and observability with dbt tests and monitors.
• Define and enforce data governance policies (PII/PHI, access controls).
• Oversee orchestration and CI/CD for pipelines with incremental strategies.

Skills

SQL

Python

dbt

BigQuery

Snowflake

Redshift

Airflow

Dagster

Prefect

Reverse-ETL

Identity resolution

Data governance

CI/CD

Education

Bachelor's degree in a relevant field

Tools

dbt Core/Cloud

Segment / CDP

Fivetran

Airbyte

Polytomic

Hightouch

Census

Looker

Job description

Job Title: Data Engineer

Location: Hyderabad

Experience: 8 – 12 Years

Employment Type: Full-Time

Shift: Overlap with US Team (evening IST preferred)

Staff Data Engineer

Data Platform & Analytics Engineering

• Hyderabad (On-site, Softobiz office)

About The Role

We are looking for a Staff Data Engineer to own our end‑to‑end data platform — from raw event ingestion, through the warehouse, to the analytics marts that the business, product and marketing teams rely on every day. You will be the person who makes sure the numbers we make decisions on are accurate, fresh, reproducible and trusted.

This is a hands‑on individual‑contributor role. You will partner daily with engineering, analytics, product, growth and external data partners. Success looks like: one canonical patient or customer key across every system, a well‑modelled warehouse that answers business questions in SQL rather than spreadsheets, and a data platform that product, marketing and leadership genuinely trust.

What You’ll Own

• Warehouse architecture and modelling — BigQuery (or equivalent) as the single source of truth, designed to serve both analytics and activation use cases.
• dbt project ownership — model layering (staging → intermediate → marts), naming conventions, tests, documentation, exposures, and the promotion workflow from dev to production.
• Ingestion pipelines — event streams from the CDP (Segment or equivalent) landing cleanly in the warehouse, plus structured loads from the product database, billing / payments, ad platforms and third‑party SaaS systems.
• Identity resolution in the warehouse — stitching anonymous, edge‑level and identified user records into one canonical key, and publishing that key to downstream consumers.
• Core marts — revenue, orders, subscriptions, cohorts, LTV, funnel conversion, channel performance, clinical or operational metrics as the business requires.
• Reverse‑ETL / activation models — warehouse tables purpose‑built to feed ad‑platform conversions APIs, lifecycle marketing, audiences and experimentation tools.
• Data quality and observability — dbt tests, freshness monitors, row‑count alerts, reconciliation checks between systems (for example: warehouse orders vs payment processor, warehouse conversions vs ad‑platform reports).
• Data governance — PII / PHI handling, column‑level masking, row‑level security where required, access policies, and the documentation that makes those policies auditable.
• Orchestration and CI/CD — scheduled runs, retries, incremental strategies, pull‑request‑based dbt reviews, automated tests in CI.
• Partnership with external data partners and the sister MarTech Engineering function — clear ownership lines, shared conventions, joint on‑call for pipeline incidents.

Required Experience

• 8–12 years in data engineering or analytics engineering at consumer‑facing digital businesses — ideally including at least one direct‑to‑consumer, e‑commerce, subscription or marketplace product.
• Deep, hands‑on experience designing and maintaining a warehouse on BigQuery, Snowflake or Redshift. You can explain partitioning, clustering, materialised views, and cost / performance trade‑offs from memory.
• Production experience with dbt — Core or Cloud — including model layering, incremental strategies, snapshots, tests, documentation, exposures, and managing a multi‑contributor repo through pull requests.
• Fluent SQL — advanced window functions, CTEs, pivoting, session stitching, slowly‑changing‑dimension patterns. You can read a 200‑line query and tell us what is wrong with it.
• Experience designing identity resolution in the warehouse — merging anonymous, cookie‑level, logged‑in and server‑generated identifiers into one canonical customer key, and publishing that key for downstream systems to join on.
• Hands‑on experience building marts for revenue, orders, subscriptions, cohorts, LTV and funnel analysis — and reconciling those marts against authoritative systems (payment processors, billing, ad platforms).
• Python for data work — transformations, orchestration DAGs, small internal tools, and scripting around the warehouse. You do not need to be a backend engineer.
• Working knowledge of at least one workflow orchestrator — Airflow, Prefect, Dagster, or equivalent — and the operational discipline that goes with it (idempotency, backfills, retries, alerting).
• Comfort integrating warehouse output into reverse‑ETL tooling (Polytomic, Hightouch or Census) so that audiences, conversion events and customer traits can be activated in ad platforms, lifecycle tools and experimentation systems.
• A bias for data contracts and tests over heroics — you treat a broken dashboard as a platform failure to be prevented, not a one‑off to be patched.
• Experience operating under ambiguity — a platform migration, re‑platforming, or a major tracking overhaul where the "right" schema had to be invented mid‑flight.

Tools and Technologies

The following stack describes what you will work with day‑to‑day. You do not need hands‑on experience with every single tool — depth in 60‑70% of this list is what we are looking for, along with the pattern‑matching to pick up the rest quickly.

• Data Warehouse: BigQuery (required), Snowflake or Redshift. Strong grasp of partitioning, clustering, cost controls.
• Transformation: dbt Core or dbt Cloud (required), dbt tests, snapshots, exposures, packages.
• Orchestration: Airflow, Prefect, Dagster, or an equivalent DAG runner. dbt Cloud scheduling for dbt‑only pipelines.
• Ingestion: Segment (CDP → warehouse), Fivetran or Airbyte (SaaS → warehouse), custom Python ingestion where needed.
• Reverse‑ETL / Activation: Polytomic (preferred), Hightouch, Census — mapping marts to ad‑platform, lifecycle and experimentation destinations.
• Product Analytics (consumer): Mixpanel, Amplitude, or Heap — you will not own these tools, but you will land their events and feed their user properties from the warehouse.
• BI / Visualisation: Omni, Looker, Mode, Metabase, Tableau — warehouse‑native semantic modelling preferred.
• Languages: SQL (advanced), Python (fluent), Jinja (dbt); shell scripting as needed.
• Storage & Cloud: Google Cloud Platform primary (Cloud Storage, IAM, Pub/Sub, Cloud Run / Cloud Functions).
• Governance & PII: Column‑level security, masking policies, row‑level access, data‑classification tagging, audit logging.
• Dev workflow: Git, GitHub / GitLab, pull‑request reviews, dbt CI, unit and schema tests, dbt docs.
• Collaboration: Linear, Slack, Google Docs & Sheets, Confluence or Notion.

Nice to Have

• Healthcare, fintech, or other privacy‑sensitive domain experience — designing a warehouse that respects PII / PHI boundaries and survives audit.
• Experience operating a dbt repo shared between an internal team and an external data partner (or vendor) — conventions, code review, ownership boundaries.
• Experience with Segment Unify, identity‑graph services, or custom identity‑resolution frameworks.
• Hands‑on with experimentation data — building the exposures, assignment and outcome tables that power A/B test readouts and feeding them to an experimentation platform.
• Hands‑on with LTV or propensity modelling (regression, survival or tree‑based), and with getting those predictions into the warehouse as first‑class tables.
• Familiarity with streaming patterns — Pub/Sub, Kafka, Kinesis — and when to prefer them over batch.
• Prior early‑hire data platform experience at a growth‑stage product company, where you set the conventions rather than inherited them.

How You Work

• You treat the warehouse as a product — versioned, documented, tested, with clear ownership of every model and every downstream exposure.
• You are as comfortable debugging a BigQuery cost regression as you are explaining an attribution or LTV model to senior leadership.
• You write dbt tests before you write dashboards. You would rather catch the problem in CI than in a Monday standup.
• You draw scope lines honestly — you know where your layer ends and the MarTech, product or ML layer begins, and you document the hand‑off.
• You reconcile aggressively — warehouse vs payment processor, warehouse vs ad platforms, warehouse vs the product UI. Anything that does not tie out is a ticket, not a footnote.
• You write things down. The team should not need to re‑learn a modelling decision you have already made.

Compensation And Logistics

• Location: On‑site at our Hyderabad (Softobiz) office. This is not a remote role.
• Reporting: Reports to the Engineering Owner, with regular working interactions across product, analytics, growth and external data partners.
• Compensation: Competitive base salary plus performance bonus and standard benefits.
• Schedule: Full‑time, with some overlap expected with partner teams in other time zones.

About Softobiz

Innovation begins with like‑minded people aiming to transform the world together. At Softobiz, we invite you to become a part of an organization that has been helping clients transform their business by fusing insights, creativity, and technology. With a team of 300+ technology enthusiasts, we have been trusted by leading enterprises around the globe for over 12+ years.

At Softobiz, we foster a culture of equality, learning, collaboration, and creative freedom, empowering our employees to grow and excel in their careers. Our technical craftsmen are pioneers in the latest technologies like AI, machine learning, and product development.

Why Should You Join Softobiz?

• Work with technical craftsmen who are pioneers in the latest technologies.
• Access training sessions and skill‑enhancement courses for personal and professional growth.
• Be rewarded for exceptional performance and celebrate success through engaging parties.
• Experience a culture that embraces diversity and creates an inclusive environment for all employees.

Softobiz is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will be afforded equal employment opportunities without discrimination based on race, creed, color, national origin, sex, age, disability, or marital status.

For more information about our solutions and organization, visit www.softobiz.com,

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